In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.
We assume that you have completed the previous lessons and have a cleaned dataset in your data folder called cleaned_cuisine.csv in the root of this 4-lesson folder.
We have loaded your notebook.ipynb file with the cleaned dataset and have divided it into X and y dataframes, ready for the model building process.
Previously, you learned about the various options you have when classifying data using Microsoft's cheat sheet. Scikit-learn offers a similar, but more granular cheat sheet that can further help narrow down your estimators (another term for classifiers):
Tip: visit this map online and click along the path to read documentation.
This map is very helpful once you have a clear grasp of your data, as you can 'walk' along its paths to a decision:
- We have >50 samples
- We want to predict a category
- We have labeled data
- We have fewer than 100K samples
- ✨ We can choose a Linear SVC
- If that doesn't work, since we have numeric data
- We can try a ✨ KNeighbors Classifier
- If that doesn't work, try ✨ SVC and ✨ Ensemble Classifiers
- We can try a ✨ KNeighbors Classifier
This is a very helpful trail to follow.
Following this path, we should start by importing some libraries to use.
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Import the needed libraries:
from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier from sklearn.model_selection import train_test_split, cross_val_score from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve import numpy as np
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Split your training and test data:
X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
Support-Vector clustering (SVC) is a child of the Support-Vector machines family of ML techniques (learn more about these below). In this method, you can choose a 'kernel' to decide how to cluster the labels. The 'C' parameter refers to 'regularization' which regulates the influence of parameters. The kernel can be one of several; here we set it to 'linear' to ensure that we leverage linear SVC. Probability defaults to 'false'; here we set it to 'true' to gather probability estimates. We set the random state to '0' to shuffle the data to get probabilities.
Start by creating an array of classifiers. You will add progressively to this array as we test.
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Start with a Linear SVC:
C = 10 # Create different classifiers. classifiers = { 'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0) }
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Train your model using the Linear SVC and print out a report:
n_classifiers = len(classifiers) for index, (name, classifier) in enumerate(classifiers.items()): classifier.fit(X_train, np.ravel(y_train)) y_pred = classifier.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100)) print(classification_report(y_test,y_pred))
The result is pretty good:
Accuracy (train) for Linear SVC: 78.6% precision recall f1-score support chinese 0.71 0.67 0.69 242 indian 0.88 0.86 0.87 234 japanese 0.79 0.74 0.76 254 korean 0.85 0.81 0.83 242 thai 0.71 0.86 0.78 227 accuracy 0.79 1199 macro avg 0.79 0.79 0.79 1199 weighted avg 0.79 0.79 0.79 1199
K-Neighbors is part of the "neighbors" family of ML methods, which can be used for both supervised and unsupervised learning. In this method, a predefined number of points is created and data are gathered around these points such that generalized labels can be predicted for the data.
The previous classifier was good, and worked well with the data, but maybe we can get better accuracy. Try a K-Neighbors classifier.
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Add a line to your classifier array (add a comma after the Linear SVC item):
'KNN classifier': KNeighborsClassifier(C),
The result is a little worse:
Accuracy (train) for KNN classifier: 73.8% precision recall f1-score support chinese 0.64 0.67 0.66 242 indian 0.86 0.78 0.82 234 japanese 0.66 0.83 0.74 254 korean 0.94 0.58 0.72 242 thai 0.71 0.82 0.76 227 accuracy 0.74 1199 macro avg 0.76 0.74 0.74 1199 weighted avg 0.76 0.74 0.74 1199✅ Learn about K-Neighbors
Support-Vector classifiers are part of the Support-Vector Machine family of ML methods that are used for classification and regression tasks. SVMs "map training examples to points in space" to maximize the distance between two categories. Subsequent data is mapped into this space so their category can be predicted.
Let's try for a little better accuracy with a Support Vector Classifier.
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Add a comma after the K-Neighbors item, and then add this line:
'SVC': SVC(),
The result is quite good!
Accuracy (train) for SVC: 83.2% precision recall f1-score support chinese 0.79 0.74 0.76 242 indian 0.88 0.90 0.89 234 japanese 0.87 0.81 0.84 254 korean 0.91 0.82 0.86 242 thai 0.74 0.90 0.81 227 accuracy 0.83 1199 macro avg 0.84 0.83 0.83 1199 weighted avg 0.84 0.83 0.83 1199✅ Learn about Support-Vectors
Let's follow the path to the very end, even though the previous test was quite good. Let's try some 'Ensemble Classifiers, specifically Random Forest and AdaBoost:
'RFST': RandomForestClassifier(n_estimators=100),
'ADA': AdaBoostClassifier(n_estimators=100)The result is very good, especially for Random Forest:
Accuracy (train) for RFST: 84.5%
precision recall f1-score support
chinese 0.80 0.77 0.78 242
indian 0.89 0.92 0.90 234
japanese 0.86 0.84 0.85 254
korean 0.88 0.83 0.85 242
thai 0.80 0.87 0.83 227
accuracy 0.84 1199
macro avg 0.85 0.85 0.84 1199
weighted avg 0.85 0.84 0.84 1199
Accuracy (train) for ADA: 72.4%
precision recall f1-score support
chinese 0.64 0.49 0.56 242
indian 0.91 0.83 0.87 234
japanese 0.68 0.69 0.69 254
korean 0.73 0.79 0.76 242
thai 0.67 0.83 0.74 227
accuracy 0.72 1199
macro avg 0.73 0.73 0.72 1199
weighted avg 0.73 0.72 0.72 1199
✅ Learn about Ensemble Classifiers
This method of Machine Learning "combines the predictions of several base estimators" to improve the model's quality. In our example, we used Random Trees and AdaBoost.
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Random Forest, an averaging method, builds a 'forest' of 'decision trees' infused with randomness to avoid overfitting. The n_estimators parameter is set to the number of trees.
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AdaBoost fits a classifier to a dataset and then fits copies of that classifier to the same dataset. It focuses on the weights of incorrectly classified items and adjusts the fit for the next classifier to correct.
Each of these techniques has a large number of parameters that you can tweak. Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.
There's a lot of jargon in these lessons, so take a minute to review this list of useful terminology!