Student_MLE_MiniProject_Trees_and_Forests.ipynb

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        "# Mini Project: Trees and Forests\n",
        "\n",
        "In this mini-project you'll be introduced to some fundamental concepts in machine learning: **Decision Trees**, **Random Forests**, **Boosting**, and **Bagging**. These techniques play a crucial role in building powerful and versatile machine learning models that can be applied to a wide range of tasks, from classification to regression.\n",
        "\n",
        "## Decision Trees\n",
        "\n",
        "Decision Trees are intuitive models that mimic human decision-making processes. Just like answering a series of questions to reach a conclusion, decision trees segment data based on a sequence of if-else questions. They partition the feature space into regions and assign a label to each region. Decision trees are particularly helpful in understanding the decision-making process behind predictions.\n",
        "\n",
        "## Random Forests\n",
        "\n",
        "Random Forests are an ensemble method that combines multiple decision trees to create a more robust and accurate model. Each tree in a random forest is trained on a random subset of the data with replacement (bootstrapping), and these trees are allowed to make individual predictions. The final prediction is determined through majority voting (classification) or averaging (regression) of the predictions made by the individual trees. Random Forests are known for their ability to handle noisy data, reduce overfitting, and capture complex relationships.\n",
        "\n",
        "## Bagging: Bootstrap Aggregating\n",
        "\n",
        "Bagging, short for Bootstrap Aggregating, is the technique behind Random Forests. It involves training multiple models on bootstrapped subsets of the training data. Bagging reduces the variance of the model by averaging out the noise and errors present in individual models. While Random Forests are a specific application of bagging to decision trees, bagging can also be applied to other base models.\n",
        "\n",
        "## Boosting\n",
        "\n",
        "Boosting is another ensemble technique that focuses on building a strong predictor by sequentially training multiple weak learners (often shallow decision trees). Boosting assigns weights to training instances, focusing more on those that the previous models misclassified. This iterative process helps the model correct its mistakes and improve its performance over time. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.\n",
        "\n",
        "In this mini-project, we will:\n",
        "\n",
        "1. Implement and visualize decision trees using the scikit-learn library.\n",
        "2. Explore the power of random forests and understand feature importance.\n",
        "3. Dive into the boosting technique with AdaBoost to enhance model accuracy."
      ]
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      "cell_type": "markdown",
      "source": [
        "For this mini-project we'll be using the [Breast Cancer Wisconsin (Diagnostic) dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html). First, let's import all the libraries we'll be using."
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Added by JMD\n",
        "from sklearn.metrics import f1_score\n",
        "# Define random_state once here so it can be changed globally\n",
        "random_state = 1"
      ],
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        "Here are your tasks:\n",
        "\n",
        "1. Use [load_breast_cancer](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html) to load the Breast Cancer Wisconsin dataset as a Pandas dataframe.\n",
        "2. Split the dataset into training and test sets.\n",
        "3. Display the first five rows of data and make sure everything looks ok. You should have already explored the data a bit in the logistic regression mini-project so there's no need to conduct further EDA.  "
      ],
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      "source": [
        "# Load the Breast Cancer Wisconsin dataset\n",
        "\n",
        "# Use as_frame = True to get data as pandas objects\n",
        "data = load_breast_cancer(as_frame = True)\n",
        "# Use X and y for feature and target variables\n",
        "X = data.data\n",
        "y = data.target\n",
        "# Swap 0 and 1 so that 1 codes for malignant (i.e., positive for cancer)\n",
        "y = 1 - y\n",
        "# Likewise swap target names by reversing the order\n",
        "target_names = data.target_names[::-1]"
      ],
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        "# Split the dataset into training and testing sets\n",
        "\n",
        "# For this exercise I've chosen to reduce the size of the test set from the default (0.3)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = random_state, stratify = y)\n",
        "print(\"training set size:\", y_train.size)\n",
        "print(\"test set size:\", y_test.size)\n",
        "print(\"test set value counts:\")\n",
        "y_test.value_counts()"
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          "text": [
            "training set size: 455\n",
            "test set size: 114\n",
            "test set value counts:\n"
          ]
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              "target\n",
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        "# Display first 5 rows\n",
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              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
              "0                 0.07871  ...         25.38          17.33           184.60   \n",
              "1                 0.05667  ...         24.99          23.41           158.80   \n",
              "2                 0.05999  ...         23.57          25.53           152.50   \n",
              "3                 0.09744  ...         14.91          26.50            98.87   \n",
              "4                 0.05883  ...         22.54          16.67           152.20   \n",
              "\n",
              "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "\n",
              "   worst concave points  worst symmetry  worst fractal dimension  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-31401b2d-267e-44e2-b587-722e224d4cf6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count classes to check for imbalance\n",
        "\n",
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "Id6uHsrYNy-5",
        "outputId": "a5445a1b-716e-48c4-ccba-aaad4bb995ce"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "0    357\n",
              "1    212\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>212</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's start by diving into decision trees. At a high level, decision trees are a machine learning algorithm used for both classification and regression tasks. They model decisions and decision-making processes by breaking down a complex decision into a sequence of simpler decisions or questions. Each decision leads to one of several possible outcomes, eventually leading to a prediction or classification.\n",
        "\n",
        "**Decision trees are built out of the following components:**\n",
        "- **Nodes:** Decision trees consist of nodes. The initial node is the root node, and the final nodes are called leaf nodes.\n",
        "- **Edges:** Edges connect nodes and represent the decision outcomes.\n",
        "- **Attributes/Features:** Each node involves a decision based on a specific attribute or feature.\n",
        "- **Splitting:** At each node, the dataset is partitioned into subsets based on an attribute's values.\n",
        "- **Leaf Nodes:** These nodes represent the final predicted class or value.\n",
        "\n",
        "**How Decision Trees Work:**\n",
        "1. **Selecting the Best Feature:** Decision trees start with the root node and select the feature that best separates the data.\n",
        "2. **Splitting:** The selected feature is used to split the data into subsets at each internal node.\n",
        "3. **Repeating:** The process continues recursively for each subset, selecting the best feature at each node.\n",
        "4. **Leaf Nodes:** The process stops when a certain stopping criterion is met, or when all instances at a node belong to the same class.\n",
        "\n",
        "**Advantages:**\n",
        "1. **Interpretability:** Decision trees are easy to understand and interpret. The path from the root to a leaf node can be visualized as a sequence of decisions.\n",
        "2. **Handles Numerical and Categorical Data:** Decision trees can handle both numerical and categorical data, making them versatile.\n",
        "3. **Non-Linearity:** They can capture non-linear relationships between features and the target variable.\n",
        "4. **Feature Importance:** Decision trees provide a measure of feature importance, helping in feature selection.\n",
        "\n",
        "**Challenges and Considerations:**\n",
        "1. **Overfitting:** Decision trees can create overly complex models that fit the training data too closely, leading to poor generalization.\n",
        "2. **Instability:** Small changes in the data can lead to different trees, which can make the model unstable.\n",
        "3. **Bias towards Majority Class:** In classification tasks, decision trees tend to favor the majority class if classes are imbalanced.\n",
        "\n",
        "**Improvements:**\n",
        "1. **Pruning:** Pruning is the process of removing branches that do not improve the model's performance on validation data, reducing overfitting.\n",
        "2. **Ensemble Methods:** Random Forests and Gradient Boosting Trees are ensemble methods that combine multiple decision trees to improve performance.\n",
        "\n",
        "There are a number of different metrics used to determine how to split a node in a decision tree. The Gini impurity is a popular criterion used in decision tree algorithms. It measures the degree of impurity in a dataset, where lower values indicate more pure subsets of data. In the context of decision trees, the Gini impurity is used to assess how often a randomly chosen element would be misclassified if it were randomly assigned to a class based on the distribution of class labels in a subset. The Gini impurity ranges between 0 (pure node, all instances belong to a single class) and 0.5 (impure node, instances are evenly distributed across classes)."
      ],
      "metadata": {
        "id": "bT64m51APSGh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here are your tasks:\n",
        "\n",
        "1. Use Scikit-Learn's [DecisionTreeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) to fit a model on the training data.\n",
        "2. Visualize the resulting tree using [plot_tree](https://scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html).\n",
        "3. Iterate on the first two steps by trying different inputs to the decision tree classifier. What happens if you change the max depth? How about the maximum number of lead nodes? From the visualization, make sure you're able to understand how to descend the decision tree to arrive at a prediction."
      ],
      "metadata": {
        "id": "PjRU4fdmTSF5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Decision Tree model\n",
        "\n",
        "DT_model = DecisionTreeClassifier(random_state = random_state)\n",
        "DT_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "41e16xuuWk_q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "e678fd49-da3e-43a6-fd19-c334e16b8106"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=1)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the Decision Tree\n",
        "plt.figure(figsize = (12, 12), dpi = 200)\n",
        "# Use feature names https://stackoverflow.com/questions/64883963/how-do-you-find-feature-names-for-decision-tree-classification\n",
        "pt_ = plot_tree(DT_model, feature_names = data.feature_names)"
      ],
      "metadata": {
        "id": "93kbxVvJEtFx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f3ff15a0-ac08-4c40-a8d2-939a17646ea6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x2400 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit tree with max_depth = 1 (a stump)\n",
        "DT_model_1 = DecisionTreeClassifier(max_depth = 1, random_state = random_state)\n",
        "DT_model_1.fit(X_train, y_train)\n",
        "pt_ = plot_tree(DT_model_1, feature_names = data.feature_names)"
      ],
      "metadata": {
        "id": "xtEs9yOqXzg5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "22df3bdb-b504-4c05-cd04-104d3add9649"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit tree with max_leaf_nodes = 4\n",
        "DT_model_2 = DecisionTreeClassifier(max_leaf_nodes = 4, random_state = random_state)\n",
        "DT_model_2.fit(X_train, y_train)\n",
        "pt_ = plot_tree(DT_model_2, feature_names = data.feature_names)"
      ],
      "metadata": {
        "id": "9OnqoTCCZWBs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "037e7512-bcf2-42b7-9954-6308a366a739"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RwfLly9G+fXsMHjwYRkZG8PDwwIsXLzBmzBhIpVJs2bIFMpkMLVu2ROXKlREWFgYdHR106NBBvqw///wTmzZtgqamJrS0tODs7Ixbt26hRo0aMDQ0BAD06dMHY8aMwT///IOqVasyDBAJjKcWEhVReTtVzdHREceOHSvRMubNm4cxY8bAxMREITUp8pTI8jZeRKWJZxMQiZSVlVWxZg8KM3v2bIUFAalUioyMDGhraytkeURUdPyagEikli5dKnQJBVhbW/OeBUQCYRggKqbo6GihS6Ai4DgRFR3DAFERmZiYQF9fH4MHDxa6FCoifX19hX2NQVSe8QBComKIj49HYmKioDXk5eXh119/xYEDBzBnzhz07NlT0HryBQcHY+7cuXBycpLfxlhoJiYmqF27ttBlECk9zgwQFUPt2rUF/eOSl5eHH3/8EQcPHixwfQJl0KJFC5ibm2P48OGoUqUK1qxZoxSBgIi+jGGASEXk5eVh1KhRCAoKwqZNmzBkyBChS/rIsGHDoKamBjc3N+Tl5WHdunUMBEQqgGGASAXk5uZi1KhR2Lx5MzZv3qzUxy0MHToU6urqGDZsGGQyGf744w8GAiIlxzBApORyc3MxcuRIbNmyBVu2bMHAgQOFLumLBg8eDDU1NQwdOlQeCIpzEyUiKlsMA0RKLDc3F8OHD8e2bduwdetWuLq6Cl1SkQ0aNAhqamoYMmQI8vLysGHDBgYCIiXFMECkpHJzc+Hm5oYdO3Zg+/bt6N+/v9AlFdvAgQOhrq6OQYMGQSaTYePGjQwEREqIYYBICeXk5GDYsGH4888/sX37dvTr10/okr7agAEDoKamhkGDBiEvLw+bNm1iICBSMgwDREomJycHQ4cOxa5du7Bjxw707dtX6JJKrH///lBXV4erqytkMpn8roZEpBzYjURKJCcnB0OGDMGePXvw559/wtnZWeiSFKZv375QU1PDgAEDkJeXhy1btjAQECkJXoGQSEnk5ORg0KBB2LdvH/7880/06dNH6JJKxd69ezFgwAA4Oztj69atDARESoBhgEgJZGdnY9CgQdi/fz927doFJycnoUsqVfv370e/fv3Qp08fbNu2jYGASGAMA0QCy87OxsCBA3Hw4EHs3r0bvXr1ErqkMnHgwAH07dsXTk5O2LZtG7S0tIQuiUi0GAaIBJSdnY0BAwYgJCQEe/bsUZqbDpWVgwcPom/fvujZsyd27NjBQEAkEIYBIoFkZWVhwIABOHToEPbu3YsePXoIXZIgQkJC4OzsjB49emDnzp0MBEQC4AXDiQSQlZWF/v374/Dhw9i3b59ogwAA9OjRA/v27cOhQ4fQv39/ZGVlCV0SkehwZoCojGVlZaFv3744duwY9u/fj65duwpdklI4fPgw+vTpgy5dumDXrl3Q1tYWuiQi0WAYICpDmZmZ6Nu3L44fP479+/ejS5cuQpekVI4cOQInJyc4Ojpi9+7dDAREZYRhgKiMZGZmwsXFBWFhYThw4AAcHR2FLkkpHTt2DL1790anTp2we/du6OjoCF0SUbnHMEBUBjIyMuDs7IxTp07h4MGD6NSpk9AlKbXQ0FD06tUL9vb22Lt3LwMBUSljGCAqZRkZGejTpw/Cw8MRHBwMBwcHoUtSCcePH0evXr1ga2uLvXv3QldXV+iSiMothgGiUpSRkQEnJydIpVKEhITA3t5e6JJUSlhYGHr27AkbGxvs27ePgYColDAMEJWS9PR09O7dG2fPnkVISAjs7OyELkklnTx5Ej169ECHDh1w4MABBgKiUsAwQFQK0tPT0atXL/z11184dOgQbG1thS5JpZ06dQrdu3fHDz/8gAMHDkBPT0/okojKFYYBIgV7+/YtevXqhb///huHDx+GtbW10CWVC+Hh4ejWrRvat2+PgwcPMhAQKRDDAJECvX37Fj179sT58+dx5MgRdOzYUeiSyhWpVIpu3bqhbdu2OHjwIPT19YUuiahcYBggUpC3b9+iR48euHjxIo4cOYIOHToIXVK5dPr0aXTt2hVt2rRBcHAwAwGRAjAMEClAWloaevTogUuXLuHo0aP44YcfhC6pXDtz5gy6du2KVq1aISQkBBUqVBC6JCKVxjBAVEJpaWno3r07Ll++jKNHj6J9+/ZClyQKf/31F7p06YL//ve/OHToEAMBUQkwDBCVQGpqKrp164bIyEgcO3YM7dq1E7okUTl37hwcHR3RsmVLHD58mIGA6CvxFsZEXyk1NRVdu3bFlStXEBoayiAggHbt2iE0NBSRkZHo2rUrUlNThS6JSCVxZoDoK7x58wZdu3bF9evXERoaitatWwtdkqidP38enTt3RrNmzXDkyBFUrFhR6JKIVArDAFExvXnzBl26dMGNGzdw/Phx/N///Z/QJRGACxcuoHPnzmjSpAmOHDmCSpUqCV0SkcpgGCAqhtevX6NLly6IiorC8ePH0apVK6FLovdcvHgRnTp1wnfffYejR4+icuXKQpdEpBIYBoiK6PXr13B0dMStW7cQFhaG77//XuiSqBCXLl1Cp06dYGVlhWPHjjEQEBUBDyAkKoJXr16hc+fOiI6OxokTJxgElFirVq0QFhaGW7duoXPnznj16pXQJREpPc4MEH1BSkoKOnfujLt37yIsLAwtW7YUuiQqgsuXL8PBwQENGzZEaGgoDAwMhC6JSGkxDBB9RkpKCjp16oSYmBicOHECLVq0ELokKobIyEjY29ujfv36CA0NhaGhodAlESklfk1A9AkvX76Eg4MDYmNjcfLkSQYBFdSiRQucOHECd+/eRadOnZCSkiJ0SURKiTMDpLLS09MxceJEvHnzBlevXsUPP/wAR0dH9OzZE+rqJcu5+UHg/v37OHnyJJo1a6aYokkQV65cgb29PerWrYvjx4/DyMjok4/19fXF9evXUa1aNbRv3x6DBw8u8PugoCDo6upiwIABpV02UZlhGCCVFxcXBz8/P5w8eRKjR49G27Zt8csvv+DYsWOQSqW4cOECxo0bBx8fH+Tl5SE1NRXLli375IVpkpOT4eDggAcPHuDkyZNo2rRpGa8RlYZr167Bzs4OderUQVhY2CcDga+vLxwdHeUXkvrll1/w+vVrpKWlYfny5di6dSt0dXWhrq6OkydPwsDAAMOHD0deXh7WrFkDmUwGIyMjzJs3ryxXj6hE+DUBlRtVqlTB1KlTC71R0NatW5GcnAxDQ0NoaGggOjq60GUkJyfD3t4e8fHxOHXqFINAOdK0aVOcPHkScXFxsLe3R3Jy8icfu2jRIkgkEpw/fx4AoK2tjX///RdXr16VPyY2NhZmZmZwc3ODpaUlfv31VxgZGaFKlSr4999/kZWVVdqrRKQwmkIXQKQo7x8clv81Qf616nNzc2FnZ4chQ4Z88vlJSUmwt7fHo0ePcOrUKfznP/8p1Xqp7DVt2hSnTp2CnZ0d7OzscOLECVSpUuWjx02dOhWtW7dGZGQkXr16hcWLF2PSpEkF7n0wY8YMREdHIzAwEN9//z1ycnIwatQo1KpVqyxXiUghODNA5VKDBg2wcOFChISEAACGDh2KsLAweHt7QyKR4PHjxwUen5iYCDs7Ozx+/Bjh4eEMAuVYkyZNEB4ejsePH8Pe3h5JSUmffGy9evUQGxuLJUuW4Pr16wV+t3btWmzYsAHJycmwsLDA7NmzMW3aNHh7e2PixImlvRpECsVjBkj08oPA06dPcerUKTRu3FjokqgM3Lx5E7a2tqhRowZOnDgBExMToUsiEgxnBkjUXrx4AVtbWzx79gzh4eEMAiLy3XffITw8HE+ePIGtrS1evHghdElEgmEYINF6/vw5bG1t8fz5c4SHh8PKykrokqiMNW7cGOHh4Xj+/Dns7OwYCEi0+DUBiVJ+EEhKSkJ4eDgaNWokdEkkoOjoaNjY2KBq1ao4efIkqlWrJnRJRGWKMwMkOs+ePYONjQ2Sk5MhlUoZBAiWlpaQSqVITEyEjY0Nnj17JnRJRGWKYYBE5enTp7CxsUFKSgqkUikaNmwodEmkJBo1agSpVIqXL1/KjyMhEguGARKNJ0+ewMbGBq9evYJUKkWDBg2ELomUTMOGDSGVSpGSkgIbGxs8ffpU6JKIygTDAIlCfhB48+YNpFIp6tevL3RJpKQaNGgAqVSK169fw8bGBk+ePBG6JKJSxzBA5V5CQgKsra2RlpbGIEBFUr9+fUilUqSmpjIQkCgwDFC59vjxY1hbWyM9PR1SqRT16tUTuiRSEfXq1YNUKkVaWhqsra2RkJAgdElEpYZhgMqtR48ewdraGpmZmZBKpbCwsBC6JFIxFhYWkEqlSE9Ph7W19UeXsSYqLxgGqFzKDwLZ2dmQSqWoW7eu0CWRisoPBJmZmbC2tsajR4+ELolI4RgGqNx5+PAhrK2tkZOTA6lUCnNzc6FLIhVXt25dSKVSZGdnw9raGg8fPhS6JCKFYhigciU+Ph7W1tbIzc2FVCpFnTp1hC6Jyglzc3NIpVLk5OTA2toa8fHxQpdEpDAMA1RuPHjwANbW1pDJZAwCVCrq1KmD06dPIy8vj4GAyhWGASoX4uLiYG1tDTU1NUilUnz77bdCl0Tl1LfffovTp08DAKytrfHgwQOBKyIqOYYBUnn5QUBdXR1SqRS1a9cWuiQq52rXrg2pVAo1NTVYW1sjLi5O6JKISoRhgFTa/fv30bFjR2hqauL06dOoVauW0CWRSOQHAg0NDQYCUnkMA6Sy7t27B2tra2hra+P06dOoWbOm0CWRyNSqVQtSqRRaWlro2LEj7t+/L3RJRF+FYYBUUmxsLKytraGjowOpVAozMzOhSyKRqlmzJqRSKXR0dNCxY0fcu3dP6JKIio1hgFROTEwMrK2toaenxyBASsHMzAzh4eHQ09NDx44dERsbK3RJRMXCMEAqJT8IVKhQAVKpFDVq1BC6JCIA/wsE+vr6sLa2RkxMjNAlERUZwwCpjLt376Jjx46oVKkSwsPD8c033whdElEBNWrUgFQqRYUKFWBtbY27d+8KXRJRkTAMkEr4999/YW1tDQMDAwYBUmrffPMNpFIpKlWqxEBAKoNhgJTenTt3YG1tDUNDQ4SHh8PU1FTokog+y9TUFFKpFAYGBujYsSPu3LkjdElEn8UwQErt9u3bsLa2hrGxMU6dOoXq1asLXRJRkVSvXh3h4eEwMjKCjY0NAwEpNYYBUlrR0dGwsbGBiYkJgwCppPxAYGxsDGtra9y+fVvokogKxTBASunWrVuwsbFB1apVcerUKVSrVk3okoi+SrVq1RAeHg4TExNYW1sjOjpa6JKIPsIwQEonKioKNjY2qF69Ok6ePImqVasKXRJRibwfaq2trXHr1i2hSyIqgGGAlMrNmzdhY2ODb775hkGAypX8QGBqagobGxtERUUJXRKRHMMAKY0bN27A1tYWZmZmOHnyJExMTIQuiUih8o9/+eabb2BjY4ObN28KXRIRAIYBUhLXr1+Hra0tatasiRMnTqBKlSpCl0RUKqpUqYKTJ0/CzMwMNjY2uHHjhtAlETEMkPCuXbsGW1tb1K5dm0GARCE/ENSqVQu2tra4fv260CWRyDEMkKCuXr0KOzs71KlTB2FhYTA2Nha6JKIyYWxsjBMnTqB27dqwtbXFtWvXhC6JRIxhgARz5coV2NnZwdzcnEGARCk/ENSpUwe2tra4evWq0CWRSDEMkCAiIyNhZ2cHCwsLhIWFwcjISOiSiARhZGSEEydOoG7durCzs8OVK1eELolEiGGAylxERATs7e1Rv359HD9+HIaGhkKXRCQoQ0NDhIWFwcLCAnZ2doiMjBS6JBIZNZlMJhO6CCr/7t27B2NjY8TExMDBwQENGzZEaGgoDAwMhC6NSGm8evUKnTt3xp07d3DixAlYWFggOTkZdevWFbo0KucYBqjU5ebmombNmujbty+2bNmCRo0aITQ0FJUrVxa6NCKl8+rVKzg6OuL27dsYOnQodu/ejYcPH0JDQ0Po0qgcYxigUnfq1CnY2dmhQoUKsLS0xKFDh3jTIaLPePbsGbp3747o6GikpaXh1KlTsLGxEbosKsd4zACVupUrVwIA1NXVERERgeXLlwtcEZFyW7ZsGSIiIqCu/m4Tnd9DRKWFMwNU6ipWrIi3b9+iU6dOcHV1hYuLCypUqCB0WURKKy0tDXv27MGOHTtw/Phx6OvrIzU1VeiyqBxjGKBSd/v2bVSpUoU3HSL6Ci9evEBSUhIaNWokdClUjjEMEBERiZym0AUou/j4eCQmJgpdBpURExMT1K5dW+gySElxe6Bc2K+KwzDwGfHx8bC0tMTbt2+FLoXKiL6+PqKjo7mBoY9we6B82K+KwzDwGYmJiXj79i22bt0KS0tLocuhUhYdHY3BgwcjMTGRGxf6CLcHyoX9qlgMA0VgaWmJFi1aCF0GESkBbg+oPOJ1BkpZXl7eRz9zdHQUoBLFCQwMxI0bNwr9nZubG54+fVrsZWZkZBTpcXfu3IGrqyvc3Nywb9++Ar+7cuUKRo4cWeD9dXNzw7BhwyCRSBAbG4v09HQMGTIEHh4e8PT0RGZmZrFrJfpaRd0eBAUFYefOnUVeblEfX9jrf056enqRHpednS3vs759+yIlJQUAMGbMGIwfPx5jxoz56Dnbt2/H2LFjMWHCBPlxGDk5OejSpQv8/PwAfNy/VHo4M1AMkydPhpeXF3bu3IkHDx5g6dKlGDlyJFauXAlPT09Uq1YNMpkMy5Ytg4ODA2xtbVGnTh1IpVIYGBhAT08Pw4cPR0xMDHx9feHi4oLvvvsOAHDjxg34+fnB1NQUdnZ2sLS0xMyZM1GlShXUqFEDM2fORKtWrTBgwADcuHEDI0eORJs2beDp6Ql9fX0YGBhgzpw5WLVqFeLj45GUlISlS5dixowZmDJlCszNzeHq6orAwEDs2bMHN27cwJs3bzBo0CDY2trK17FVq1bo378/4uPj0bVrV9ja2uLHH3+EkZERXr58iXXr1uHp06dIS0uDr68vkpKSUL16daSmpmL69Om4evUq/P390bt3b2zZskW+3nPnzv3o/bx+/Tp27dqF+Ph4TJs2DY0bN/7iGPj7+8Pf3x9mZmbo3r07+vTpI/9d8+bNsX79+gIbVx0dHeTk5EBDQwMmJia4ffs2TE1NsXjxYqxfvx779u2Dq6trST4WJFKluT2QSqVITU1FTk4OmjdvjjVr1kAmk8HIyAjTpk3D4MGDsXnzZnh6emL69OkFHh8TEwNHR0e0bt0ajo6OOHbsWIHXf/v27Sf7HwCysrJw9OhRHDlyBHp6eli2bNkX3wstLS1s2rQJALB06VJcuHABlSpVgpmZGWbNmoX58+fj77//Rtu2bQG8+8pl48aNaNGiBbS0tOT3KFm8eDH69OmDpKQkAB/3L5UehoFicHBwQFhYGOLi4pCbm4tz586hbdu22LZtG/r164devXph8uTJiIqKQm5uLry8vKCvr4+NGzdi1KhRcHBwgJGREerVqwdfX98Cy/7111+xcuVK+Qfe29sb06dPR9OmTdG3b1+8efMGOjo6mDRpEu7evYtVq1bhzZs3qF+/PiZPnlxgWRoaGnj16hWOHz+OESNGYOPGjfD09ISenh40NTWxevVqdO/eHQYGBjh37lyBjYGamhomTZqEvLw89OrVCzk5OWjWrBm8vLywYsUKhIaGFnit3r17w87ODl27doWhoSGaNWuGKVOmwNTUFPPmzZOv9/vOnz+PqVOnonfv3vDw8ICZmRmAdxdamTVrVoHHtmnTBv3795f/OyEhQf74/Kuzfc7q1auhrq6O48ePIyAgAHPmzMHBgwcxceJEpKenw9zc/IvLICpMaW4PrK2toauriwEDBmDgwIGoX78+1NTUcPv2bWhpaeHXX39F27ZtMW3aNFhZWRV4/IfLAiB/fZlMho4dO36y/+fNm4eLFy9i9OjR+O2336CjowMACA8Px8GDBwss08vL66P+iYuLw4ULFzB27Fjs3btX/l2+ubk54uPj5WEgNjYW6urqWLhwIbZt24Zt27bB3NwcJiYmqF+/vjwMfNi/ha0bKQa/JiiGDh06IDQ0FLq6uqhfvz4CAwNhb28P4N0f0ff/V1tbG/r6+gCAkJAQmJiYoE+fPnjz5o38MR/68I/bh4/Lv2qflpaWfFr9/cckJSVBKpVi/vz5+OGHH5CamoqWLVvi2rVr2LRpE4YOHQqZTAZjY2P4+vpi7ty58PHxKfAaubm5kMlkyM3NlU8pfrhuhdWU/9j31+HD9c7XtGlTeHh44M6dO1i6dCnOnj2L/Mtd5OTkFPgvNze3wOvVqFEDCQkJAICiXCIjvx5TU1P5e+/r64uAgADUq1ePB4LRVyvN7cH7fZSTk4NRo0bB19cXO3fuhLa2Nh49eoRq1arh5cuXHz1eR0cH2dnZACC/amH+63+p/wcPHox27dohJCQEK1euRHx8PIB3/f1hb37Yfzdu3MBPP/2E9evXQ1tbG7Vr18bDhw8BAA8ePChwkF/NmjXlOz5VqlTB69evERoaiqioKKxcuRKHDh1CTEzMR/1LpYczA8Wgr6+PpKQkDBgwABYWFli5ciUaNGiAqlWrYty4cThz5gyysrIKTHdnZ2dj4sSJ0NfXR61ataCnp4fWrVtj4sSJGD58OJo0aQIAmDlzJiZMmIAaNWrAxsYGHh4emD17NkxMTNC0aVNUqlTpo3o6deqE4OBgTJ06FUZGRpg6dSo0NDSwePFiXLx4Ed27dwcA2NvbY8OGDfD29oaamhq6d+8OiUQir+X9PW8tLS34+PggLi4OXl5esLW1hbu7O6ZOnYrnz5/D3d0d//zzzyffo/bt28PHxweDBw/Gn3/+WWC9338fBw4ciIEDB+Lly5fYu3cvKlasiObNm3/xGuxTpkzB1KlToauri+HDhwN4971iQEAA3rx5g/nz5+PWrVuYMmUK/P394eXlhaysLDx//hz+/v4AgPHjxyMnJwfa2tofzaoQFVVpbg+aN28OX19fpKenY/bs2Zg2bRrMzMyQk5ODKVOmYN26dTh+/DgmTJiAf/75p8Dju3XrhsWLFyMiIkK+h52vYsWKn+3/unXrYubMmQDe/XHftm0bZsyYATs7O9jZ2X3yvUhNTYWtrS26d+8Ob29vjBgxAu3atcP27dsxadIkZGZmom3btjhy5AgePnwId3d3NGjQQH68wG+//QZjY2MA774iuXDhAurVq1do/1Lp4BUIPyMyMhItW7ZERESEaI4ezv+OUYzEON5UdPx8KBeOh2LxawIqQKxBgIhIzBgGypnPnfY3e/bsj6YNv+Rzp/I9f/4cgwYNgkQikU/vb9q0CQMGDICrqyu2bdsmf+yaNWvQsmXLYq4NEb2vLPt7586dGD16NAYOHIiNGzcCAH7//Xd4eHjAyckJZ86cAVD4KYKkenjMgAqLiYnBjBkz0LBhQxw9ehSHDx/+5Gl/fn5+iI+Plx9YVFSfO5Vv3bp1GDNmDNq1awcnJye4u7vj6NGjWL9+PdTV1TFkyBAMGjQIN27cQFpaGu9aSFQMQvf3gAEDMGDAAMhkMjg6OmL48OHy6wVEREQgJCQEVlZWhZ4iSKqHYUCFBQYGwsfHB02aNEFERMRHv3//tL/ClPRUvvj4ePkRwtWqVUNSUhIGDx4MGxsbAMCiRYuQkZGBlStX4vfff8fx48e/fmWJREbo/s43b948eHh4yP89c+ZMnD17Fr/99luhpwi6ubkVd1VJCfBrAhVXnNP+ClOSU/neP3XoxYsXqFKlCpYtW4bz58/jwoULWLRoEf755x+kpaVh8uTJuH37NrZv3/71K0skMkL2t0wmw9SpU/Hdd9+hd+/e8p/Pnz8fe/bswYIFCwo9RZBUE2cGVJhEIsHMmTPRsGFDPHr0qNDTDz+nQoUKJTqVb/To0Zg8eTK2bdsGW1tbaGlpoUePHhg9ejRkMhns7Ozwww8/4IcffgAA3L59GwMHDvy6lSUSGaH7e9WqVQgLC8Pr169x7do1zJs3D3PnzkViYiJSUlIgkUhgZmb20SmCpJp4auFnKPupK69evUJAQABSU1NRsWJFXp2rhJR9vElYZf35YH9/HvtVsTgzoMIMDAy4gSAqp9jfVJZ4zICIlcbdE69du4YRI0Zg1KhR8PLyAgAEBwdDIpHA3d0d3377LQDA19cX7u7ucHZ2xuXLlxVeB5HYlUZ/p6enY8SIEQXuSXD16lX06tULI0eOhLe3N4DCT0Ek5caZARWxa9cunDx5EgYGBvLv9rZs2YK0tDQ0b94cbm5uaNOmDTp16oSrV6+ic+fOePDgAR4/foytW7fCzc0NNWvWhJ6eHjQ0NDB9+nT5svfv34/Tp08jIyMDHTt2RIsWLTB37lzUqlUL7du3R48ePYpcZ9OmTbFhwwYA7452Tk9PR8+ePdGzZ08cP35cfuRy/h5PZGQkdu3ahf/+978KeqeIVI+q9Leenh42bNhQIGicP38eEokEXbp0kZ/Z8OEpiB06dFDE20SliGFARcTGxsLMzAwuLi6wtLTE/fv3kZmZCUNDQwQFBcHNzQ1qamqYO3cuQkNDIZVKsXDhQri6uspvZjJw4EBYWVnByckJmZmZ8mUvWrQInTt3BgBcunQJNWvWhJqaGnr06CG/y1i+devWISoqSv5vDQ0NLFmy5KN6jx07hkaNGhW4J8HatWsLHNCUkZGBJUuW4Oeff1bMm0SkolStv9/XuXNn9O3bF0uXLi1w/4L3T0Ek5ccwoCJmzJiB6OhoBAYG4vvvv8elS5fg7u6ORo0aoXXr1gCAypUrA3h317L8i3/o6OjINwz5FyTJysoqsGw1NTXMmTOnwOlL9erVw5EjR7B582asXbtW/vPc3Fzk5OTI/13Y8ac7duzAzZs34efnJ/9ZXFwc9PT0YGpqCgBISUnBmDFj4OPjg7p16379G0NUDqhSf38oICAAmzdvRuPGjeXhxMjICPPnz8ezZ88wbtw47Nq162veFipDDAMqYu3atbh79y6Sk5NhYWGBypUrIyAgAObm5tDULNowbt68GdnZ2WjXrp38PuUAMGHCBIwYMQLGxsawsLCApaUlQkJCkJWVhWbNmhVYhkQi+exrnDlzBpMmTUKvXr0gkUjg5+cHQ0ND+XeI+VxcXKCjo4Ply5ejffv2GDx4cNHfDKJyRlX6O395t27dgkQiwezZs+Hi4gJfX1+YmJhAT08PhoaGH52CSMqPpxZ+Rnk6dcXNzQ1+fn7yPXP6WHkab1I8Zf58iLG/lXk8VBFnBkQiKChI6BKIqJSwv6mkeGohERGRyDEMqKi4uLhS+y7O3t5eftR/YdcDCA0NxdixYzF+/HjcvXsXwLtTicaPHy8/pagwO3bsQLdu3eQHFmZnZ2PYsGGQSCTo27cvUlJSkJKSIp/yJCrPhOzh5s2bQyKRwNPT86Pnrly5EhKJBIMGDcLz588LXf6VK1cwcuTIAqcYurm5yfs5NjYWeXl5GDJkCEaPHg0nJyf5PRA+9TrsfWExDCghT09P3L9/HwDg6uqKV69eYfv27Zg2bRpGjRqFBw8eyB/7/gYlKCgIO3fuREJCAsaPHw8vLy+MHz/+o5uTfImmpibGjh0L4N2GZM2aNZg1axb27NmDvLw8LF68GPr6+tDW1ka1atVw7tw5mJmZ4bfffkPNmjXx999/F7pcV1dX+UVJAEBLSwubNm1CYGAg2rRpgwsXLsDQ0JB3PSOVp8w9DLy7b0Fubi5q1KhR4HnZ2dkICwtDYGAgPD098ccffxS6/ObNm2P9+vUFfqajowNNTU1oaGjAxMREftOidevWwdHREf/8889nX4e9LyyGASU0YsQIbNy4Ec+ePYOenh4MDAygoaEBmUwGNTU17N69+7PPX7FiBdTV1WFkZIRXr14VSOQPHz7EhAkTCvwXFhb22eXlXw/gxx9/xIsXL5CQkAA/Pz84OTkhICCgwK2Mzc3NER8fX6z1jYuLw4ULF2Bra1us5xEpK2XuYeDdWT/r1q1DWloapFKp/HGJiYnyuxAWt5dXr16N9evXo1evXggICJCvc/fu3bFz584C1yAoyetQ6eABhEqoZcuWmDdvHjZt2oShQ4cCANasWYNTp04hODgYkZGR8sfq6OjIzy9OTU2Frq4ucnNz4ezsLL9b4PtkMlmB84iBz98C9cPrAWRnZ6NGjRpQV1eX37K0du3aOH36NADgwYMHxbra2I0bN7Bw4UKsX78e2traRX4ekTJT5h4GAHX1d/uBpqamePPmjfyxJiYmSEpKAvCul/NDflF8uMzIyEhUrVoVQUFBOHr0KNavXy+/RHlJXodKB8OAkrK3t8eGDRvk0+r169fHggULcO/ePfklfYF3jZeTk4OlS5fi/PnzcHZ2hpeXF7y9vREcHIyXL19i9erV0NLSAgDUrl37i7c1fV9h1wNwcXGBp6cnUlJSMG/ePFhYWGD79u2YNGkSMjMz0bZtW1y/fh137txB37595cs6ceIEfvvtNzx//hzGxsYYOHAgbG1t0b17d3h7e2PEiBFo1aqVgt5BImEpaw87Ojpi4sSJqFixIt6+fQsPDw9cv34du3fvxs8//wx7e3uMHTsWKSkpWLp0KQDAx8enwJVC4+PjMX/+fNy6dQtTpkyBv78/vLy8kJWVhefPn8Pf3x+mpqZ4/PgxPD09kZCQgJ9//vmLr0MCktEnRUREyADIIiIihC6lTHXu3LnEy1i9erUsKirqq54bHh4uW7BgQYlrKC6xjjcVjSp9PhTRw/mioqJkq1evVtjyPqc4va9K46EKeMwAfcTKyqpYex6FkUgksLKyKvbzUlJScODAAU4bEpWAInr4/WWVxVUE2fvC4tcERRAdHS10CWUq/9LA73+vWZbyv2Mt69cX2zjT11GFz4nQPfy1itP7qjAOqoRh4DNMTEygr6/P6+aLiL6+vvwoZ6L3cXugfNivisN7E3xBfHw8EhMThS5DId68eYPu3bvLD9griUWLFuHIkSMICQlBpUqVFFSh8ExMTDhNSZ+kzNsDMfY3+1VxGAZEZN68efKjmb/55psSLSshIQEWFhaYOXMmfHx8FFQhEX0t9jeVBMOASKSkpKBOnToYPnw4AgICFLLMCRMmYNOmTbh//z4MDQ0VskwiKj72N5UUzyYQiWXLliEzMxNTp05V2DKnTZuGjIwMLF++XGHLJKLiY39TSTEMiMDLly8REBAADw+PEk8fvu+bb76BRCJBQEAAUlJSFLZcIio69jcpAsOACAQEBCA7OxvTpk1T+LKnTZuGrKwshU1NElHxsL9JERgGyrnk5GQsX74cY8aMQfXq1RW+fFNTU3h4eGDZsmV4+fKlwpdPRJ/G/iZFYRgo5/L3GhT5XeKHpk6diuzsbO49EJUx9jcpCsNAOZa/1+Dp6Ylq1aqV2utUr14dY8aMwbJly5CcnFxqr0NE/8P+JkViGCjHlixZgtzc3BJfgKQopk6ditzcXN59jKiMsL9JkRgGyqnExET89ttvGDt2bKnuNeSrVq0aPD09sXz5cvl9yomodLC/SdEYBsqpJUuWQCaTlcleQz5vb2/IZDIsWbKkzF6TSIzY36RoDAPlUGJiIlasWIFx48aV6U08qlatirFjx2LFihVKe/12IlXH/qbSwDBQDvn7+0NNTQ2TJ08u89eeMmUK9x6IShH7m0oDw0A58+LFC6xcubLM9xrymZiYYNy4cVixYgVevHhR5q9PVJ6xv6m0MAyUM4sXL4a6urogew35pkyZAjU1Nfj7+wtWA1F5xP6m0sIwUI48f/4cq1atwvjx41GlShXB6qhSpQrGjx+PlStX4vnz54LVQVSesL+pNDEMlCOLFi2CpqYmJk2aJHQpmDx5MjQ0NLB48WKhSyEqF9jfVJoYBsqJZ8+e4ffff4eXlxeMjY2FLgfGxsbw8vLCqlWruPdAVELsbyptDAPlxKJFi6ClpYWJEycKXYrcxIkToaWlhUWLFgldCpFKY39TaWMYKAeePn2K1atXY8KECTAyMhK6HLn8vYfff/8dT58+FbocIpXE/qaywDBQDixcuBDa2tpKtdeQb+LEidDW1ubeA9FXYn9TWWAYUHFPnjxBYGAgJk6cCENDQ6HL+YiRkREmTJiA1atX48mTJ0KXQ6RS2N9UVhgGVJyfnx90dXUxYcIEoUv5pAkTJkBHRwcLFy4UuhQilcL+prLCMKDCEhISsGbNGkyaNAkGBgZCl/NJhoaGmDRpEtasWcO9B6IiYn9TWWIYUGF+fn7Q09PD+PHjhS7li7y8vKCrqws/Pz+hSyFSCexvKksMAyrq8ePHWLt2LSZPnqzUew35DAwM5HsPjx8/FrocIqXG/qayxjCgohYsWIAKFSqoxF5DPi8vL+jr63PvgegL2N9U1hgGVNDDhw+xbt06TJ48GZUrVxa6nCKrXLkyJk+ejLVr1+LRo0dCl0OklNjfJAQ1mUwmE7oIKp4xY8Zg165duH//PipVqiR0OcXy+vVrmJubY8CAAVi1apXQ5RApHfY3CYEzAyrm4cOH+OOPPzBlyhSV21AA7/YepkyZgj/++AMPHz4UuhwipcL+JqFwZkDFeHh4YPfu3Sq515DvzZs3MDc3R79+/fD7778LXQ6R0mB/k1A4M6BCHjx4gPXr18Pb21tlNxQAUKlSJfneQ3x8vNDlECkF9jcJiTMDKsTd3R379u3D/fv3UbFiRaHLKZHU1FSYm5vD2dkZgYGBQpdDJDj2NwmJMwMqIi4uDhs2bMDUqVNVfkMBABUrVoS3tzc2bNiABw8eCF0OkaDY3yQ0zgyoiNGjRyM4OBj37t1DhQoVhC5HIdLS0mBubo7evXtj7dq1QpdDJBj2NwmNMwMq4P79+wgKCsLUqVPLzYYCACpUqICpU6di48aNiIuLE7ocIkGwv0kZcGZABYwaNQohISHlaq8hX1paGurWrYuePXti3bp1QpdDVObY36QMODOg5O7du4egoCBMmzat3G0ogP/tPQQFBeH+/ftCl0NUptjfpCw4M6DkRowYgSNHjuDevXvQ19cXupxS8fbtW9StWxfdunXD+vXrhS6HqMywv0lZcGZAicXExGDz5s2YPn16ud1QAIC+vj6mTZuGTZs2ITY2VuhyiMoE+5uUCWcGlNjw4cMRGhqK2NhY6OnpCV1OqUpPT0fdunXRpUsXbNiwQehyiEod+5uUCWcGlFRMTAy2bNmC6dOnl/sNBQDo6elh+vTp2Lx5M2JiYoQuh6hUsb9J2XBmQEkNGzYMYWFhothryJeeng4LCwt06tQJQUFBQpdDVGrY30FCl0Mf4MyAEvr333+xdetWzJgxQzQbCuB/ew9btmzB3bt3hS6HqFSwv9nfyogzA0poyJAhCA8PR0xMDHR1dYUup0xlZGTAwsICdnZ22Lx5s9DlECkc+5v9rYw4M6Bk7ty5g+3bt2PGjBmi21AAgK6uLmbMmIFt27bhzp07QpdDpFDsb/a3suLMgJIZPHgwTp8+jZiYGOjo6AhdjiAyMjJQr1492NjYYMuWLUKXQ6Qw7G/2t7LizIASuX37Nnbs2IGZM2eKdkMBvNt7mDlzJrZv3869Byo32N/vsL+VE2cGlMjAgQPx119/4e7du6LeWABAZmYm6tWrhw4dOmDbtm1Cl0NUYuzv/2F/Kx/ODCiJW7duYefOnaLfa8ino6ODmTNnYseOHYiOjha6HKISYX8XxP5WPpwZUBIDBgzA+fPncffuXWhrawtdjlLIzMxE/fr10a5dO+zYsUPocoi+Gvv7Y+xv5cKZASUQFRWFXbt2YdasWdxQvEdHRwezZs3Cn3/+iaioKKHLIfoq7O/Csb+VC2cGlED//v1x8eJF/Pvvv9xYfCArKwsNGjRA69atsXPnTqHLISo29vensb+VB2cGBHbz5k3s3r0bP/30EzcUhdDW1sasWbOwa9cu7j2QymF/fx77W3lwZkBgffv2xeXLl/Hvv/9CS0tL6HKUUv7eQ6tWrbBr1y6hyyEqMvb3l7G/lQNnBgR0/fp17NmzBz/99BM3FJ+hra2Nn376Cbt378aNGzeELoeoSNjfRcP+Vg6cGRCQs7Mzrl69itu3b3Nj8QXZ2dlo2LAhWrRogT179ghdDtEXsb+Ljv0tPM4MCOTatWvYt28f9xqKSEtLCz/99BP27t2L69evC10O0Wexv4uH/S08zgwIpE+fPrh+/Tpu374NTU1NoctRCdnZ2WjUqBGaNWuGvXv3Cl0O0Sexv4uP/S0szgwI4OrVq9i/fz98fHy4oSgGLS0t+Pj4YN++fbh69arQ5RAViv39ddjfwuLMgAB69+6NqKgoREdHc2NRTDk5OWjUqBH+85//YP/+/UKXQ/QR9vfXY38LhzMDZSwyMhIHDx7kXsNX0tTUhI+PDw4cOIArV64IXQ5RAezvkmF/C4czA2WsZ8+euH37Nm7dusWNxVfKycmBlZUVLC0tcfDgQaHLIZJjf5cc+1sYnBkoQxEREQgJCcHs2bO5oSiB/L2H4OBgREZGCl0OEQD2t6Kwv4XBmYEy1KNHD9y9exdRUVHQ0NAQuhyVlpOTg8aNG6Nhw4YIDg4Wuhwi9rcCsb/LHmcGysg///yDQ4cOYfbs2dxQKICmpiZmz56NkJAQXL58WehySOTY34rF/i57nBkoI926dcO9e/dw8+ZNbiwUJDc3F40bN0a9evVw6NAhocshEWN/Kx77u2xxZqAMXLx4EUeOHOFeg4JpaGhg9uzZOHz4MC5duiR0OSRS7O/Swf4uW5wZKANdunTBgwcPcOPGDW4sFCw3Nxf/+c9/UKdOHRw5ckTockiE2N+lh/1ddjgzUMouXLiAY8eOYc6cOdxQlIL8vYejR4/i4sWLQpdDIsP+Ll3s77LDmYFS5ujoiEePHuH69etQV2f2Kg25ublo0qQJateujaNHjwpdDokI+7v0sb/LBj+9pej8+fMIDQ3FnDlzuKEoRRoaGpgzZw6OHTuG8+fPC10OiQT7u2ywv8sGZwZKUadOnfDkyRNcu3aNG4tSlpeXhyZNmsDMzAyhoaFCl0MiwP4uO+zv0sdPcCk5d+4cwsLCuNdQRtTV1TFnzhwcP34cf//9t9DlUDnH/i5b7O/Sx5mBUmJvb48XL17gypUr3FiUkby8PDRr1gzVq1dHWFiY0OVQOcb+Lnvs79LFT3EpOHv2LE6ePMm9hjKWv/dw4sQJ/PXXX0KXQ+UU+1sY7O/SxZmBUmBnZ4ekpCRERkZyY1HG8vLy0Lx5c1StWhUnTpwQuhwqh9jfwmF/lx5+khXszJkzOHXqFHx9fbmhEIC6ujp8fX1x8uRJnD17VuhyqJxhfwuL/V16ODOgYDY2NkhJSUFkZCTU1NSELkeU8vLy0KJFCxgbG+PUqVNCl0PlCPtbeOzv0sFoq0BSqRRSqRS+vr7cUAgof+8hPDwcp0+fFrocKifY38qB/V06ODOgQNbW1njz5g0uX77MjYXAZDIZWrZsCQMDA4SHhwtdDpUD7G/lwf5WPM4MKEh+SuVeg3JQU1ODr6+vfG+OqCTY38qF/a14nBlQAJlMho4dOyI9PR2XLl3ixkJJyGQyfP/996hQoQKkUinHhb4K+1s5sb8VS1PoAlRZbm4ufv31V7Ro0QJnz57FoUOH+IFUIvl7Dz169MCRI0cQERGBWbNm8e5yVCTsb+XG/lYszgyUQExMDOrXr4/GjRtDR0cHe/fuRZ06dYQui94TFxeHPn36IDs7Gzdv3kRMTAwsLCyELotUAPtb+bG/FYfHDJRAfo6KiorCkydP0L9/f4Erog/1798fT58+xc2bNwH8b8yIvoT9rfzY34rDMFAC73/watSoge3btwtYDRVm+/bt+Oabb+T/5saCior9rfzY34rDMFACOjo6AIDevXvj3LlznJ5SQhYWFvj777/Ru3dvAIC2trawBZHKYH8rP/a34vCYgRLKyMiArq6u0GVQEXCsqLj4mVEdHKuSYRggIiISOX5NQEREJHJKe52B+Ph4JCYmCl2G0jAxMUHt2rWFLqNMieUzIMaxfZ9YxvlDYhp3sY7xpyjj2CtlGIiPj4elpSXevn0rdClKQ19fH9HR0Ur3ASotYvoMiG1s3yemcf6QWMZdzGP8Kco49koZBhITE/H27Vts3boVlpaWQpcjuOjoaAwePBiJiYlK9eEpTWL5DIhxbN8nlnH+kJjGXaxj/CnKOvZKGQbyWVpaokWLFkKXQQLiZ0AcOM7lH8dYuZX7AwgDAwNx48aNQn83e/ZsJCUlFWt5d+7cgaurK9zc3LBv374Cv3v+/DkGDRoEiUSClStXAgB+//13eHh4wMnJCWfOnPm6laAvKstxvnLlCkaOHAlHR0f5z1atWoVhw4Zh9OjRuHDhAgBgwoQJGDlyJJycnD5ZG32dshxvAHj9+jVatGiBnTt3AgDGjRsHd3d39OnTB3FxcUhPT4e7uzvc3d3Rr18/pKenF3+l6JOE7u9z585h0KBBGDRoEM6dOwcACA0NxdixYzF+/HjcvXu3mGukfJR6ZqC4YmJiMGPGDDRs2BBHjx7F4cOH8fTpU6SlpcHX1xdJSUmoXr06UlNT4efnh/j4eGRnZxfrNfz9/eHv7w8zMzN0794dffr0kf9u3bp1GDNmDNq1awcnJye4u7tjzJgxAICIiAiEhISgQ4cOCl1nMRJ6nJs3b47169cX2FgcPHgQx44dQ1ZWFlxcXHDo0CHExsYiJCQEoaGhOHXqFP7zn/8o7D0QE6HHGwB+/vlnDBo0SP7vFStWAAD27duHY8eOQSKRYM2aNQDehcAnT56gbt26JVxzcRJ6vAvrb39/f3kQHDhwINq0aYPFixejRYsWyMvLQ7Vq1RSz8gIqV2EgMDAQPj4+aNKkCSIiIj76fe/evWFnZ4euXbsW+vy0tDTMmjWrwM/atGlT4JrkCQkJMDMzAwCoqxecWImPj5d/B1StWjUkJSXB1NQUM2fOxNmzZ/Hbb7+VaP3oHaHHuTDe3t7w8PBA1apVkZaWBgBo2rQpOnXqhFevXuHAgQNFXT36gNDjvXXrVtjZ2eHp06cFfp6cnIzt27dj3bp1AIBbt25h6dKleP36dYFL5FLxCD3ehcnMzJRfkTI7OxsvXrxAQkICjh8/jvPnzyMgIAC+vr5FXUWlVO6+Jsi/xWhhtxqtUKECACAvL++Tz8/JySnwX25uboHf16hRAwkJCQA+vg527dq18fDhQwDAixcvUKVKFQDA/PnzsWfPHixYsOAr14o+JOQ4F8bBwQFr1qyBp6enPAjeuXMHx48fR1BQEBYtWlTkdaOPCTneZ8+exbFjx7B9+3Zs3rwZycnJePjwITw8PLBixQoYGRkBAKysrPDHH3+gZcuWCA8P//qVJaXrbx0dHWRlZSErKwtaWlowNjZGjRo1oK6ujipVquD169dFXjdlVa5mBiQSCWbOnImGDRvi0aNHqFSpUrGeX6FCBfl3/Z8yZcoUTJ06Fbq6uhg+fDgAwM3NDQEBARg9ejQmT56Mbdu2wdbWFlpaWpg7dy4SExORkpICiUTy1etG/yP0OL958wbz58/HrVu3MGXKFPj7+2PHjh04e/YsUlJS8Ouvv6JKlSowMjKCh4cHXrx4If+6iIpP6PHOn/4PCgqCrq4ujI2N0apVK7Rs2RJz585Fr1690KBBAyxfvhx5eXlITU3leJeA0ONdWH9PmTIFo0aNAgBMmjQJWlpacHFxgaenJ1JSUjBv3ryvW1klopSXI46MjETLli0RERFRrKNPX716hYCAAKSmpqJixYoqP22T72vfD1X2uXUuT+MsxrF9X1HWvzyNdz4xjXtx17U8jvf7lHXsy9XMgIGBQbn74NDHOM7iwvEWF463MMrdMQNf4/2jRhXl2rVrGDFiBEaNGgUvLy8A7+6qNXXqVIwbN05+NDJPPVQOpfEZeP78Ofr164cxY8Zg2rRpCl8+FU9Z9fmJEyfg4eGBQYMG8aBhAZTGOKenp2PEiBEwNzeX/6yw7XlhpyCqCpWbGdi1axdOnjwJAwMD+Xc9W7ZsQVpaGpo3bw43Nze0adMGnTp1wtWrV9G5c2c8ePAAjx8/xtatW+Hm5oaaNWtCT08PGhoamD59unzZ+/fvx+nTp5GRkYGOHTuiRYsWmDt3LmrVqoX27dujR48eRa6zadOm2LBhA4B3R7+mp6fjjz/+wJs3b6Cvr4+aNWsCAE89/Aqq8hk4d+4cOnbsCE9PT/j4+OD8+fNo06aNwt+P8khVxriwPre3t4e9vT1yc3Ph4uKC8ePHK/bNKUdUZZz19PSwYcOGAkFj3bp1H23PPzwFsV27dop4m8qEyoWB2NhYmJmZwcXFBZaWlrh//z4yMzNhaGiIoKAguLm5QU1NDXPnzkVoaCikUikWLlwIV1dXvHz5EsC7QbKysoKTkxMyMzPly160aBE6d+4MALh06RJq1qwJNTU19OjRA23bti1Qx7p16xAVFSX/t4aGBpYsWfJRvceOHUOjRo2gp6eHW7duoUOHDnB1dUXPnj3h6OgIPT09nnpYTKryGejSpQtmzZqFSZMm4enTp4iPj2cYKCJVGeN87/c58O5gw23btmHIkCEKf2/KE1Ub5/cVtj3/8BREVaJyYWDGjBmIjo5GYGAgvv/+e1y6dAnu7u5o1KgRWrduDQCoXLkygHengxgYGMj/f/4HJX+QsrKyCixbTU0Nc+bMKXA6S7169XDkyBFs3rwZa9eulf88NzcXOTk58n8Xdhzmjh07cPPmTfj5+QEAatWqBWNjYwDvjnjNysqCnp4e5s+fj2fPnmHcuHHYtWtXyd4gEVCVz4Curq58gzJ27Fhel70YVGWMgY/7HHh3ZPqwYcNgbW2NoUOHlui9KM9UaZw/VNj2PP8URADQ0tIq3pshMJULA2vXrsXdu3eRnJwMCwsLVK5cGQEBATA3N4emZtFWZ/PmzcjOzka7du3kKQ54d+WwESNGwNjYGBYWFrC0tERISAiysrLQrFmzAsv40mmCZ86cwaRJk9CrVy9IJBL4+fnJTz08cuQIrKysYGBgwFMPv4KqfAays7MxevRoaGtr49tvv0WTJk2Kva5ipSpjXFifHz58GBcvXkRGRgacnJyKve5ioirjnL+8W7duQSKRYPbs2YVuzz88BVGlyJRQRESEDIAsIiJC4cseNmyY7MmTJwpfbmkqzfdDWYnlMyDGsX1faa2/Mo1xYcQ07mLp5aJS1rFXuZmBkgoKChK6BBIYPwPlH8dYHDjOilNuTi2Mi4srtWl2e3t7+RWtfH194e7uDmdnZ1y+fBnAuxtbSCQSeHp6Anh3R6z8q1N96TK0hw4dkl/HPCUlBW5ubgW+e6SiK6vPwI4dO9CtW7fPjtObN2/g4uKCH3/8Ef369UNaWhquXr0Ka2tr+V0N6esIOc5FuVNdcHAwJBIJ3N3d8e233wIADhw4gEaNGpVKzeVRWY1xYXcb/VBhpxUGBQXBwcEBEokER48eBfDu5lUdOnSQn00AvPtqQVW+/lWJMODp6Yn79+8DAFxdXfHq1Sts374d06ZNw6hRo/DgwQP5Y9//EAUFBWHnzp1ISEjA+PHj4eXlhfHjx390neov0dTUxNixYwG8CwNr1qzBrFmzsGfPHgDvDh7Jzc1FjRo1AABHjhzB8OHDsWrVKkRFReHx48eFLvfJkyc4f/48mjZtCgAwNDSEm5tbsWoTC2X6DLi6usLb2/uzj3/48CEsLCywdu1aWFhYICYmBs2aNYO1tXWxXldslHmc8/LysHjxYujr60NbW/uTd6rr2bMnAgMD4ezsjJEjRwJ4d9phnTp1ilVLeaVMY3zw4EFs3LgRK1aswC+//FLo4/NPK2zYsKH8Z2pqaqhQoQLS09PlgW/cuHEYMWJEgedOmDChWLUJSSW+JhgxYgQ2btwIT09P6OnpwcDAABoaGpDJZFBTU8Pu3bvh4uLyyeevWLEC6urqMDQ0xP3795GQkIBatWoBeLfR/vAUkm7dusHBweGTy8vIyMCSJUvw888/A3h3EJG6ujpmzpwJqVSKIUOGwNfXF6dOnUJSUhIePXokv0NWPplMhnnz5sHf3x/Ozs5f+9aIhrJ9Br6kbt26uHnzJnr27AlNTU189913X70sMVHmcS7unerWrl37xWvki5EyjXFhdxstiiFDhmDYsGFITEzEiBEjEBwcXOTnKiuVCAMtW7bEvHnzsGnTJvlpOmvWrMGpU6cQHByMyMhI+WN1dHTkp5qkpqZCV1cXubm5cHZ2xg8//PDRsmUyWYFTSoDP3w0rJSUFY8aMgY+Pj/x+5fm3wDQ1NcWbN29gYmIi3wj06dMHFhYWHy0nNjYWiYmJmDVrFm7fvo2VK1fK0yp9TJk+A0Vx+PBhdO3aFZ6enli9ejUOHTqEXr16lWiZYqDM41ycO9XFxcVBT08PpqamRV6+WCjTGDs4OMDBwQFPnjz55Nc+hcnf5hsYGKjc9QQ+RSXCAPDue54NGzbIp+3q16+PBQsW4N69ewX2uk1NTZGTk4OlS5fi/PnzcHZ2hpeXF7y9vREcHIyXL19i9erV8nNAa9euXaz07uLiAh0dHSxfvhzt27eHo6MjJk6ciIoVK+Lt27fw8PDA48ePMWfOHOTk5KB79+4wMTFBWFgYdHR05FcYrFevHnbv3g0AuH37NoNAESjLZ+DEiRP47bff8Pz5cxgbG+PHH3+Ej4+PfKYIANq3bw9PT0/cunULCQkJ+P333xX0LpR/yjzOhd2p7sOxB/53mXEqnLKM8Yd3GwUKH88PTys8cOAArl+/juTkZIwbNw4A8Oeff2LTpk3Q1NSElpaW6s34CnYew2co26kXnTt3LvEy5s6dK3vx4sUXHxceHi5bsGBBgZ8p2/tRFpRtnT/3GcjLy5ONHz++SMuZM2eO7Pz58/J/K9t6ljVlW//i9npUVJRs9erVxV62sq13aVK2dVVULxfF/fv3Ze7u7gV+pmzvRz6VOIBQaFZWViX+7m/27NkwMTH57GNSUlJw4MAB1K5du0SvRYr3uc+Ampoali9f/sVlXL16FXFxcfIrqpHyKW6vW1lZFelo8QMHDnDclYQiermoAgMD0aBBA4UtrzSpzNcEQlq6dGmZvI6hoSGWLVtWJq9FxaOIz0CzZs14XrSSK61e7927N3r37l0qy6biKavtOQCVOk1cqcNAdHS00CUoBTG/D+V93cv7+hWV2N4Hsa0vIM51Loyyvg9KGQZMTEygr6+PwYMHC12K0tDX1//i1wzliZg+A2Ib2/eJaZw/JJZxF/MYf4oyjr2aTFaE2zMJID4+HomJiUKXoTRMTExEdyyBWD4DYhzb94llnD8kpnEX6xh/ijKOvdKGASIiIiobPJuAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5P4f3i8pRYAmocwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forests are an extension of decision trees that leverage the power of ensemble learning to improve predictive accuracy and robustness. Instead of relying on a single decision tree, Random Forests combine multiple decision trees to make more accurate and stable predictions. Let's walk through the steps of going from decision trees to Random Forests:\n",
        "\n",
        "**Step 1: Building Individual Decision Trees**\n",
        "1. **Decision Tree Construction:** Start by building multiple individual decision trees. Each tree is trained on a bootstrapped subset of the training data, meaning that each tree sees a slightly different version of the data due to random sampling with replacement.\n",
        "2. **Feature Selection:** At each node of a decision tree, only a random subset of features is considered for splitting. This introduces randomness and diversity among the trees.\n",
        "\n",
        "**Step 2: Combining Trees in a Random Forest**\n",
        "1. **Predictions:** When you need to make a prediction using the Random Forest, each individual tree in the forest makes a prediction.\n",
        "2. **Majority Voting (Classification) or Averaging (Regression):** In the case of classification, the class that the majority of trees predict becomes the final prediction. In the case of regression, the average of predictions from all trees is taken as the final prediction.\n",
        "\n",
        "**Benefits of Random Forests:**\n",
        "1. **Reduced Overfitting:** The ensemble nature of Random Forests helps reduce overfitting. The diversity among the trees reduces the risk of capturing noise in the data.\n",
        "2. **Feature Importance:** Random Forests can provide an estimate of feature importance by tracking how much each feature contributes to improving the purity of the nodes.\n",
        "3. **Handles Noisy Data:** Random Forests can handle noisy and irrelevant features more effectively than individual decision trees.\n",
        "4. **Parallelization:** Training decision trees can be parallelized since they are independent of each other, which speeds up the training process."
      ],
      "metadata": {
        "id": "JbkDq7ApWF8m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here are your tasks:\n",
        "\n",
        "1. Use your training data to train a Random Forest using [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html).\n",
        "2. Extract the feature importances from the trained model.\n",
        "3. Print the feature importances from largest to smallest."
      ],
      "metadata": {
        "id": "WJGS-FUzWiTz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train random forest model\n",
        "\n",
        "RF_model = RandomForestClassifier(random_state = random_state)\n",
        "RF_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "srHwa2q-WpWt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "af839ad7-47d9-4e35-fec8-b908b84d9b18"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=1)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract feature importances\n",
        "\n",
        "# This just gets a pandas array without names\n",
        "importances = RF_model.feature_importances_\n",
        "# This creates a pandas series with names\n",
        "forest_importances = pd.Series(importances, index = data.feature_names)"
      ],
      "metadata": {
        "id": "rLO_P-ivWc33"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print feature importance\n",
        "\n",
        "# Print the values sorted from largest to smallest\n",
        "forest_importances_sort = forest_importances.sort_values(ascending = False)\n",
        "print(forest_importances_sort)\n",
        "# Make a bar plot to visualize the importances\n",
        "forest_importances_sort.plot.barh()"
      ],
      "metadata": {
        "id": "yc_9uoazFmxR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0b7b3e6e-bbf8-45ad-9bfd-899f6558ea36"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "worst concave points       0.116656\n",
            "worst perimeter            0.109035\n",
            "worst radius               0.108366\n",
            "worst area                 0.107679\n",
            "mean concave points        0.102401\n",
            "mean concavity             0.072188\n",
            "mean radius                0.066237\n",
            "mean perimeter             0.053515\n",
            "mean area                  0.046446\n",
            "area error                 0.028159\n",
            "worst concavity            0.027801\n",
            "worst texture              0.019556\n",
            "mean texture               0.017677\n",
            "radius error               0.017643\n",
            "worst compactness          0.013233\n",
            "worst symmetry             0.011268\n",
            "worst smoothness           0.010894\n",
            "worst fractal dimension    0.010334\n",
            "texture error              0.007231\n",
            "perimeter error            0.007009\n",
            "mean smoothness            0.006598\n",
            "concave points error       0.005769\n",
            "mean compactness           0.005308\n",
            "concavity error            0.005082\n",
            "symmetry error             0.004825\n",
            "fractal dimension error    0.004709\n",
            "compactness error          0.003937\n",
            "smoothness error           0.003930\n",
            "mean symmetry              0.003831\n",
            "mean fractal dimension     0.002684\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "AdaBoost, short for Adaptive Boosting, is a powerful ensemble learning technique used primarily for classification tasks. It is designed to enhance the performance of weak learners (typically shallow decision trees) by combining their predictions. AdaBoost focuses on learning from the mistakes of previous models and gives more weight to misclassified instances, allowing subsequent models to correct those mistakes.\n",
        "\n",
        "Here's an overview of how AdaBoost works:\n",
        "\n",
        "**Step 1: Building Weak Learners (Base Models)**\n",
        "1. **Initialization:** Each training instance is initially assigned equal weights.\n",
        "2. **Training Weak Learners:** A series of weak learners (e.g., shallow decision trees) are trained on the data. Each weak learner tries to minimize the weighted classification error, where the weight of an instance depends on its previous misclassifications. The first weak learner is trained on the original data.\n",
        "\n",
        "**Step 2: Weighting Instances**\n",
        "1. **Calculating Error:** The weighted classification error of each weak learner is computed as the sum of weights of misclassified instances.\n",
        "2. **Updating Weights:** Instances that were misclassified by the previous weak learner are given higher weights, making them more important for subsequent models. Instances that were correctly classified are given lower weights.\n",
        "\n",
        "**Step 3: Combining Weak Learners**\n",
        "1. **Predictions and Voting:** The predictions from all weak learners are combined using weighted majority voting. Weights are assigned to each weak learner based on its performance (lower error leads to higher weight).\n",
        "2. **Final Prediction:** The final prediction is made by taking a weighted majority vote of the weak learners' predictions.\n",
        "\n",
        "**Benefits of AdaBoost:**\n",
        "1. **Adaptive Learning:** AdaBoost focuses on instances that were misclassified by previous models, adapting to the complexity of the data.\n",
        "2. **Improves Weak Models:** Even if individual weak learners perform only slightly better than random guessing, AdaBoost can combine them to create a strong model.\n",
        "3. **Reduces Overfitting:** The iterative nature of AdaBoost allows it to focus on hard-to-classify instances and improve overall generalization."
      ],
      "metadata": {
        "id": "wBO7q_tdZ3iT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here are your tasks:\n",
        "\n",
        "1. Build and train an [AdaBoostClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) on your training data using a decision tree of max depth equal to 1 as your weak learner.\n",
        "2. Evaluate your decision tree, random forest, and AdaBoost models by applying [accuracy_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) to the test data. Which model performs the best? Experiment by changing the hyperparameters of these models.\n"
      ],
      "metadata": {
        "id": "T8IsKNfIaAC3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build and train an AdaBoostClassifier on your training data.\n",
        "\n",
        "# Initialize decision tree classifier with max depth equal to 1\n",
        "DTC = DecisionTreeClassifier(max_depth = 1)\n",
        "AB_model = AdaBoostClassifier(estimator = DTC, random_state = random_state)\n",
        "AB_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "cEmNpWHLLZIQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "outputId": "bb266087-9876-4a7d-c2f7-1011089bda10"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostClassifier.html\">?<span>Documentation for AdaBoostClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   random_state=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate models with default parameters\n",
        "\n",
        "print(\"Accuracy of models with default hyperparameters:\")\n",
        "\n",
        "DT_predict = DT_model.predict(X_test)\n",
        "DT_accuracy = accuracy_score(DT_predict, y_test)\n",
        "print(f\"DecisionTree: {DT_accuracy:.2f}\")\n",
        "\n",
        "RF_predict = RF_model.predict(X_test)\n",
        "RF_accuracy = accuracy_score(RF_predict, y_test)\n",
        "print(f\"RandomForest: {RF_accuracy:.2f}\")\n",
        "\n",
        "AB_predict = AB_model.predict(X_test)\n",
        "AB_accuracy = accuracy_score(AB_predict, y_test)\n",
        "print(f\"AdaBoost: {AB_accuracy:.2f}\")\n",
        "\n",
        "# Also calculate F1 scores\n",
        "print(\"\\nF1 score of models with default hyperparameters:\")\n",
        "DT_F1 = f1_score(DT_predict, y_test)\n",
        "RF_F1 = f1_score(RF_predict, y_test)\n",
        "AB_F1 = f1_score(AB_predict, y_test)\n",
        "print(f\"DecisionTree: {DT_F1:.2f}\")\n",
        "print(f\"RandomForest: {RF_F1:.2f}\")\n",
        "print(f\"AdaBoost: {AB_F1:.2f}\")"
      ],
      "metadata": {
        "id": "chOiS6grF4At",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea2743b0-3400-49f4-fccb-140e53d65c36"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of models with default hyperparameters:\n",
            "DecisionTree: 0.93\n",
            "RandomForest: 0.96\n",
            "AdaBoost: 0.95\n",
            "\n",
            "F1 score of models with default hyperparameters:\n",
            "DecisionTree: 0.90\n",
            "RandomForest: 0.94\n",
            "AdaBoost: 0.93\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate models after adjusting hyperparameters\n",
        "\n",
        "print(\"Accuracy of models with max_leaf_nodes = 8:\")\n",
        "\n",
        "DT_model = DecisionTreeClassifier(max_leaf_nodes = 8, random_state = random_state)\n",
        "DT_model.fit(X_train, y_train)\n",
        "DT_predict = DT_model.predict(X_test)\n",
        "DT_accuracy = accuracy_score(DT_predict, y_test)\n",
        "print(f\"DecisionTree: {DT_accuracy:.2f}\")\n",
        "\n",
        "RF_model = RandomForestClassifier(max_leaf_nodes = 8, random_state = random_state)\n",
        "RF_model.fit(X_train, y_train)\n",
        "RF_predict = RF_model.predict(X_test)\n",
        "RF_accuracy = accuracy_score(RF_predict, y_test)\n",
        "print(f\"RandomForest: {RF_accuracy:.2f}\")\n",
        "\n",
        "DTC = DecisionTreeClassifier(max_leaf_nodes = 8)\n",
        "AB_model = AdaBoostClassifier(estimator = DTC, random_state = random_state)\n",
        "AB_model.fit(X_train, y_train)\n",
        "AB_predict = AB_model.predict(X_test)\n",
        "AB_accuracy = accuracy_score(AB_predict, y_test)\n",
        "print(f\"AdaBoost: {AB_accuracy:.2f}\")\n",
        "\n",
        "# Also calculate F1 scores\n",
        "print(\"\\nF1 score of models with max_leaf_nodes = 8:\")\n",
        "DT_F1 = f1_score(DT_predict, y_test)\n",
        "RF_F1 = f1_score(RF_predict, y_test)\n",
        "AB_F1 = f1_score(AB_predict, y_test)\n",
        "print(f\"DecisionTree: {DT_F1:.2f}\")\n",
        "print(f\"RandomForest: {RF_F1:.2f}\")\n",
        "print(f\"AdaBoost: {AB_F1:.2f}\")"
      ],
      "metadata": {
        "id": "qvw6WURR5dYn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac1df6cd-13ff-4d94-bf8a-a59385f10725"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of models with max_leaf_nodes = 8:\n",
            "DecisionTree: 0.92\n",
            "RandomForest: 0.95\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AdaBoost: 0.98\n",
            "\n",
            "F1 score of models with max_leaf_nodes = 8:\n",
            "DecisionTree: 0.89\n",
            "RandomForest: 0.93\n",
            "AdaBoost: 0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Write-up by Jeffrey Dick\n",
        "\n",
        "## Loading data\n",
        "\n",
        "This uses the same breast cancer dataset as in the previous mini-project. Similarly to that project, I swapped the target values so that 0 and 1 code for benign and malignant, respectively. To make the train-test split I decreased the test size to 0.2 (the default is 0.3) because this is a fairly small dataset and I want to use more data for training.\n",
        "\n",
        "## Data exploration\n",
        "\n",
        "Data exploration was covered in more depth in the previous project. Briefly, printing the first five rows shows that the features have different scales, but this isn't a problem for decision tree-based methods. As before, we have 357 benign and 212 malignant cases so there isn't a large imbalance between the classes. However, to be on the safe side, I have used `stratify = y` when splitting the data to keep the same imbalance between classes in the train and test sets.\n",
        "\n",
        "**NOTE**: For splitting the dataset and running all models the `random_state` parameters is set to obtain reproducible results.\n",
        "\n",
        "## Decision trees\n",
        "\n",
        "We start by fitting a decision tree using the default parameters for `DecisionTreeModel()`. The split at the root node is on *worst perimeter*, and this has a high Gini index (0.468), indicating high impurity in the classifications at this node. Nevertheless, the majority of samples (260 out of 272) with *worst perimeter* <= 105.95 are benign, and most of the others (158 out of 183) are malignant. At the next level, the splits are on *worst concave points* and *worst texture*, and these have lower Gini impurity (0.084 and 0.236, respectively) than the root node. The Gini impurity of all the leaf nodes is 0, indicating perfect classification of the training data. A drawback of such a complex decision tree is overfitting and low accuracy on test data.\n",
        "\n",
        "Although each branch is interpretable at face value, an intuitive understanding of such as large tree is rather difficult. By setting max_depth to 1 we create a much simpler tree with only one branch (a stump). The stump is easy to interpret, but has a lower classification accuracy. In contrast to the previous tree where each of the 15 leaf nodes had a Gini index of 0, the leaves in the sump have non-zero Gini indexes, indicating some impurity in the classifications. However, an advantage of the stump is that it avoids overfitting.\n",
        "\n",
        "We can make a somewhat more complex tree by setting the maximum number of leaf nodes to 4. Just like the stump, the root node in this tree splits on *worst perimeter*, but the splits on the next level are on *worst concave points* and *worst texture*. Although these features are used in this small decision tree, they were not identified as having high feature importance in the logistic regression project - this shows how different ML algorithms can leverage distinct subsets of features.\n",
        "\n",
        "## Random forests\n",
        "\n",
        "In the first iteration above, the random forest is created with default parameters. *worst concave points* is the feature with the highest importance. This feature was also present in the individual decision trees created above. Other features with high importance include *worst perimter*, *worst radius*, and *worst area*, which were identified previously in the logistic regression project. Therefore, feature importance in logistic regression and Random Forests overlap to some extent, but there are still differences, especially with respect to *worst concave points*, which is more important in Random Forests.\n",
        "\n",
        "## AdaBoost and model evaluation\n",
        "\n",
        "For models created with default hyperparameters, there is a slightly higher accuracy for Random Forest (0.96) than for AdaBoost (0.95), and both of these outperform Decision Tree (0.93). After a few attempts at modifying the hyperparmeters, I settled on using `max_leaf_nodes = 8` for all models. This causes a small decrease in accuracy of Decision Tree and Random forest, but results in a notable improvement for AdaBoost with an accuracy of 0.98.\n",
        "\n",
        "I also calculated the F1 score for all the models and got similar results. The main difference is that the F1 score shows relatively low performace for the Decision Tree model.\n",
        "\n",
        "Finally, I evaluated the effect of different random seeds. This table shows the F1 scores for models with different values of the `random_state` parameter and maximum leaf nodes set to 8 or, for AdaBoost only, default values of the `max_leaf_nodes` parameter.\n",
        "\n",
        "| `random_state`         | 1    | 2    | 3    | 4    | 5    |\n",
        "|------------------------|------|------|------|------|------|\n",
        "| DecisionTree           | 0.89 | 0.93 | 0.90 | 0.91 | 0.85 |\n",
        "| RandomForest           | 0.93 | 0.93 | 0.94 | 0.89 | 0.89 |\n",
        "| AdaBoost               | 0.98 | 0.94 | 0.99 | 0.94 | 0.90 |\n",
        "| AdaBoost (default)     | 0.93 | 0.98 | 0.94 | 0.92 | 0.93 |\n",
        "\n",
        "Note that `random_state` affects both the train/test split and the model generation process. For this dataset, AdaBoost consistently performs better than the other models. Setting `max_leaf_nodes = 8` only improves the F1 score for AdaBoost in 3 out of 5 cases, so it is not clear that this is the best way to tune the model."
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