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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1OJd-uRJ-vJVOujMl3cLjQPLb5xG1P84F","timestamp":1727969318770}],"authorship_tag":"ABX9TyMaVzM7fe8nQGsR8lCXsLjD"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# k-최근접 이웃 회귀"],"metadata":{"id":"ihvIqTanAPUl"}},{"cell_type":"code","source":["import numpy as np\n","perch_length = np.array([8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0, 21.0,\n","       21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5, 22.5, 22.7,\n","       23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5, 27.3, 27.5, 27.5,\n","       27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0, 36.5, 36.0, 37.0, 37.0,\n","       39.0, 39.0, 39.0, 40.0, 40.0, 40.0, 40.0, 42.0, 43.0, 43.0, 43.5,\n","       44.0])\n","perch_weight = np.array([5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0, 110.0,\n","       115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0, 130.0,\n","       150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0, 197.0,\n","       218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0, 514.0,\n","       556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0, 820.0,\n","       850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0, 1000.0,\n","       1000.0])"],"metadata":{"id":"c-mv0s5EAWGT","executionInfo":{"status":"ok","timestamp":1727969504225,"user_tz":-540,"elapsed":475,"user":{"displayName":"조예인","userId":"17650117334011908449"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","plt.scatter(perch_length, perch_weight)\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":618},"id":"4QEv_AvBAbtf","executionInfo":{"status":"ok","timestamp":1727972967407,"user_tz":-540,"elapsed":1188,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"d67b8937-6151-44af-b662-60ecb42aaf95"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<function matplotlib.pyplot.show(close=None, block=None)>"],"text/html":["<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n","      pre.function-repr-contents {\n","        overflow-x: auto;\n","        padding: 8px 12px;\n","        max-height: 500px;\n","      }\n","\n","      pre.function-repr-contents.function-repr-contents-collapsed {\n","        cursor: pointer;\n","        max-height: 100px;\n","      }\n","    </style>\n","    <pre style=\"white-space: initial; background:\n","         var(--colab-secondary-surface-color); padding: 8px 12px;\n","         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n","\n","Parameters\n","----------\n","block : bool, optional\n","    Whether to wait for all figures to be closed before returning.\n","\n","    If `True` block and run the GUI main loop until all figure windows\n","    are closed.\n","\n","    If `False` ensure that all figure windows are displayed and return\n","    immediately.  In this case, you are responsible for ensuring\n","    that the event loop is running to have responsive figures.\n","\n","    Defaults to True in non-interactive mode and to False in interactive\n","    mode (see `.pyplot.isinteractive`).\n","\n","See Also\n","--------\n","ion : Enable interactive mode, which shows / updates the figure after\n","      every plotting command, so that calling ``show()`` is not necessary.\n","ioff : Disable interactive mode.\n","savefig : Save the figure to an image file instead of showing it on screen.\n","\n","Notes\n","-----\n","**Saving figures to file and showing a window at the same time**\n","\n","If you want an image file as well as a user interface window, use\n","`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n","``show()`` the figure is closed and thus unregistered from pyplot. Calling\n","`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n","limitation of command order does not apply if the show is non-blocking or\n","if you keep a reference to the figure and use `.Figure.savefig`.\n","\n","**Auto-show in jupyter notebooks**\n","\n","The jupyter backends (activated via ``%matplotlib inline``,\n","``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n","the end of every cell by default. Thus, you usually don&#x27;t have to call it\n","explicitly there.</pre>\n","      <script>\n","      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n","        for (const element of document.querySelectorAll('.filepath')) {\n","          element.style.display = 'block'\n","          element.onclick = (event) => {\n","            event.preventDefault();\n","            event.stopPropagation();\n","            google.colab.files.view(element.textContent, 401);\n","          };\n","        }\n","      }\n","      for (const element of document.querySelectorAll('.function-repr-contents')) {\n","        element.onclick = (event) => {\n","          event.preventDefault();\n","          event.stopPropagation();\n","          element.classList.toggle('function-repr-contents-collapsed');\n","        };\n","      }\n","      </script>\n","      </div>"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","train_input, test_input, train_target, test_target = train_test_split(\n","    perch_length, perch_weight, random_state=42\n",")"],"metadata":{"id":"jJVk6G_yNvor","executionInfo":{"status":"ok","timestamp":1727973059346,"user_tz":-540,"elapsed":511,"user":{"displayName":"조예인","userId":"17650117334011908449"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["test_array = np.array([1,2,3,4])\n","print(test_array.shape) # (4,) 배열을 (2,2) 크기로 바꾸기"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dRixaXodOBvb","executionInfo":{"status":"ok","timestamp":1727973126729,"user_tz":-540,"elapsed":492,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"585faaba-5ccb-46f5-f055-0d5a1a544027"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["(4,)\n"]}]},{"cell_type":"code","source":["test_array = test_array.reshape(2,2)\n","print(test_array.shape)\n","\n","# 지정한 크기와 원본 배열의 원소 개수가 다르면 에러 발생."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1bvQvie2OW3l","executionInfo":{"status":"ok","timestamp":1727973188238,"user_tz":-540,"elapsed":598,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"1045b8e9-811f-4860-d703-fcd434a40fbb"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["(2, 2)\n"]}]},{"cell_type":"code","source":["# train_input 의 크기는 (42, ), 2차원 배열인 (42,1) 로 바꾸기 위해 train_input.reshpe(42,1) 과 같이 사용.\n","# 크기에 -1을 지정하면, 나머지 원소 개수로 모두 채우라는 의미.\n","# 예시로, 첫 번째를 나머지 원소 개수로 채우고, 두 번째 크기를 1로 하려면 train_input.reshape(-1,1) 처럼 사용.\n","train_input= train_input.reshape(-1,1)\n","test_input = test_input.reshape(-1,1)\n","print(train_input.shape, test_input.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LMLShSDDOlr7","executionInfo":{"status":"ok","timestamp":1727973468922,"user_tz":-540,"elapsed":470,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"34c9fa0d-eac4-4257-fe9d-07256c06aaaa"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["(42, 1) (14, 1)\n"]}]},{"cell_type":"markdown","source":["# 결정계수 (R^2)"],"metadata":{"id":"PHEEibqbPv5R"}},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsRegressor\n","knr = KNeighborsRegressor()\n","\n","# k-최근접 이웃 회귀 모델 훈련 : 회귀에서는 정확한 숫자를 맞히는 것이 거의 불가능함.\n","# 이를 '결정계수' 라고 부름, 간단히 R^2 라고 함.\n","knr.fit(train_input, train_target)\n","print(knr.score(test_input, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pMheO4w5P1wo","executionInfo":{"status":"ok","timestamp":1727973677702,"user_tz":-540,"elapsed":494,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"7f53b625-d733-41df-b12f-d891e1963d47"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["0.992809406101064\n"]}]},{"cell_type":"code","source":["# score() 메서드의 출력값의 의미 : 출력값이 높을수록 좋은 것. 정확도, 결정계수 등 ...\n","# 만약, score() 메서드가 에러율을 반환한다면 실제로는 낮은 에러가 높은 값이 되도록 바뀌는 것.\n","\n","# 사이킷런 패키지 : sklearn.metrics 패키지 아래 여러 측정 도구\n","# mean_absolute_error : 타깃과 예측의 절댓값 오차를 평균하여 반환\n","from sklearn.metrics import mean_absolute_error\n","\n","# 테스트 세트에 대한 예측을 만든다.\n","test_prediction= knr.predict(test_input)\n","\n","# 테스트 세트에 대한 평균 절댓값 오차를 계산한다\n","mae = mean_absolute_error(test_target, test_prediction)\n","print(mae)\n","\n","# 19 = 예측이 평균적으로 19g 정도 타깃값과 다르다는 것을 의미."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Bi-vn8l0QdYi","executionInfo":{"status":"ok","timestamp":1727974568520,"user_tz":-540,"elapsed":587,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"c956a910-8bc3-472c-9fac-e80a04cd96bc"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["19.157142857142862\n"]}]},{"cell_type":"markdown","source":["# 과대적합 vs 과소적합"],"metadata":{"id":"1AP_CQtKVhZ6"}},{"cell_type":"code","source":["print(knr.score(train_input, train_target))\n","# 훈련 세트에서 더 좋은 점수가 나와야 하는데 왜 점수가 더 낮을까 ..?\n","# 과소 적합 : 너무 단순한 모델 때문.\n","# 과대 적합 : 훈련 세트에만 잘 맞는 모델."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D46xKYa3T216","executionInfo":{"status":"ok","timestamp":1727975037301,"user_tz":-540,"elapsed":586,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"fbed7134-06cc-4077-a5da-a4fb99d6997c"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9698823289099254\n"]}]},{"cell_type":"code","source":["# n_neighbors 속성값을 바꾸기\n","\n","# 이웃의 개수를 3으로 설정.\n","knr.n_neighbors=3\n","\n","# 모델을 다시 훈련\n","knr.fit(train_input, train_target)\n","print(knr.score(train_input, train_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ypow2lMgVpS-","executionInfo":{"status":"ok","timestamp":1727975632180,"user_tz":-540,"elapsed":463,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"465660b0-9e79-46d7-ca00-f5f0753d5ffc"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9804899950518966\n"]}]},{"cell_type":"code","source":["print(knr.score(test_input, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qRJm4uUBX6kB","executionInfo":{"status":"ok","timestamp":1727975653504,"user_tz":-540,"elapsed":458,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"83c84cdf-5980-4c13-d3f3-3aa35cf41cec"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9746459963987609\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"QqUvztdPX_ib"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git "a/week2/[MLNovice]\354\241\260\354\230\210\354\235\270_week2-2.ipynb" "b/week2/[MLNovice]\354\241\260\354\230\210\354\235\270_week2-2.ipynb"
new file mode 100644
index 0000000..e6a738a
--- /dev/null
+++ "b/week2/[MLNovice]\354\241\260\354\230\210\354\235\270_week2-2.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPiEOMy/pyUZswL09vL5qn5"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# 선형 회귀 : k- 최근접 이웃의 한계"],"metadata":{"id":"6OgfIHNWaCOk"}},{"cell_type":"code","source":["import numpy as np\n","perch_length = np.array(\n","    [8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0,\n","     21.0, 21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5,\n","     22.5, 22.7, 23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5,\n","     27.3, 27.5, 27.5, 27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0,\n","     36.5, 36.0, 37.0, 37.0, 39.0, 39.0, 39.0, 40.0, 40.0, 40.0,\n","     40.0, 42.0, 43.0, 43.0, 43.5, 44.0]\n","     )\n","perch_weight = np.array(\n","    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0,\n","     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0,\n","     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0,\n","     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0,\n","     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0,\n","     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0,\n","     1000.0, 1000.0]\n","     )"],"metadata":{"id":"kg1h9wXAaEzr","executionInfo":{"status":"ok","timestamp":1728034254549,"user_tz":-540,"elapsed":993,"user":{"displayName":"조예인","userId":"17650117334011908449"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","\n","# 훈련 세트와 테스트 세트로 나눈다.\n","train_input, test_input, train_target, test_target = train_test_split(\n","    perch_length, perch_weight, random_state=42\n",")\n","\n","# 훈련 세트와 테스트 세트를 2차원 배열로 바꿈\n","train_input = train_input.reshape(-1,1)\n","test_input = test_input.reshape(-1,1)"],"metadata":{"id":"OkOBmuzkaNCH","executionInfo":{"status":"ok","timestamp":1728034255594,"user_tz":-540,"elapsed":4,"user":{"displayName":"조예인","userId":"17650117334011908449"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsRegressor\n","\n","knr = KNeighborsRegressor(n_neighbors=3)\n","\n","# k- 최근접 이웃 회귀 모델을 훈련\n","knr.fit(train_input, train_target)"],"metadata":{"id":"PxDVicu8bIwM","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1728034267797,"user_tz":-540,"elapsed":3,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"0c74dadb-5421-465b-b8fb-6b65e7092939"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsRegressor(n_neighbors=3)"],"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>KNeighborsRegressor(n_neighbors=3)</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;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_neighbors=3)</pre></div> </div></div></div></div>"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["print(knr.predict([[50]]))\n","# 농어의 무게를 1.033g 정도로 예측하였으나, 실제 농어의 무게는 훨씬 더 많이 나감."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"91W9v-4E3dPk","executionInfo":{"status":"ok","timestamp":1728034270917,"user_tz":-540,"elapsed":4,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"73be6337-4a50-4481-f90e-3d63b9a4759d"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["[1033.33333333]\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","# 50cm 농어의 이웃 구하기\n","distances, indexes = knr.kneighbors([[50]])\n","\n","# 훈련 세트의 산점도를 그림\n","plt.scatter(train_input, train_target)\n","\n","# 훈련 세트 중에서 이웃 샘플만 다시 그리기\n","plt.scatter(train_input[indexes], train_target[indexes], marker='D')\n","\n","# 50cm 농어 데이터\n","plt.scatter(50, 1033, marker='^')\n","plt.xlabel('lenght')\n","plt.ylabel('weight')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"bYHOvBnM3mmc","executionInfo":{"status":"ok","timestamp":1728034688183,"user_tz":-540,"elapsed":1530,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"f4c0959c-76d2-43d7-a942-3a5a4cbdc48a"},"execution_count":12,"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":["print(np.mean(train_target[indexes]))\n","print(knr.predict([[100]]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fn8jbIZp4aaa","executionInfo":{"status":"ok","timestamp":1728034883794,"user_tz":-540,"elapsed":405,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"e6b5993c-0109-4c90-8b3a-33ec43deaa84"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["1033.3333333333333\n","[1033.33333333]\n"]}]},{"cell_type":"code","source":["# 100cm 농어의 이웃을 구함.\n","distances, indexes = knr.kneighbors([[100]])\n","\n","# 훈련 세트의 산점도를 그림\n","plt.scatter(train_input, train_target)\n","\n","# 훈련 세트 중에서 이웃 샘플만 다시 그림\n","plt.scatter(train_input[indexes], train_target[indexes], marker='D')\n","\n","# 100cm 농어 데이터\n","plt.scatter(100, 1033, marker='^')\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"WFdZz0bK516h","executionInfo":{"status":"ok","timestamp":1728035067175,"user_tz":-540,"elapsed":865,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"9852129e-da2d-4e57-c57a-0308d6e38f5f"},"execution_count":18,"outputs":[{"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":["# 선형 회귀"],"metadata":{"id":"WHQXzqi_68AO"}},{"cell_type":"code","source":["from sklearn.linear_model import LinearRegression\n","lr = LinearRegression()\n","\n","# 선형 회귀 모델 훈련\n","lr.fit(train_input, train_target)\n","\n","# 50cm 농어에 대한 예측\n","print(lr.predict([[50]]))\n","\n","print(lr.coef_, lr.intercept_) # 머신러닝에서는 기울기를 계수 또는 가중치라고 함"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7o3mIkLn6BPt","executionInfo":{"status":"ok","timestamp":1728035234001,"user_tz":-540,"elapsed":400,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"24452a27-409b-4f15-d414-a5c75c10ee48"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["[1241.83860323]\n","[39.01714496] -709.0186449535477\n"]}]},{"cell_type":"code","source":["# 훈련 세트의 산점도를 그림\n","plt.scatter(train_input, train_target)\n","\n","# 15에서 50까지 1차 방정식 그래프를 그림\n","plt.plot([15, 50], [15*lr.coef_+lr.intercept_, 50*lr.coef_+lr.intercept_])\n","\n","# 50cm 농어 데이터\n","plt.scatter(50, 1241.8, marker='^')\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"BTsfi3I87G5H","executionInfo":{"status":"ok","timestamp":1728035407036,"user_tz":-540,"elapsed":888,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"2d5ac909-de43-42fd-e026-89b28f96227e"},"execution_count":22,"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":["print(lr.score(train_input, train_target))\n","print(lr.score(test_input, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ury71hV070T0","executionInfo":{"status":"ok","timestamp":1728035452843,"user_tz":-540,"elapsed":397,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"c4742ad9-2083-4136-ee9e-4473e1591c95"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["0.939846333997604\n","0.8247503123313558\n"]}]},{"cell_type":"markdown","source":["# 다항 회귀"],"metadata":{"id":"ZBj1StUm8Olv"}},{"cell_type":"code","source":["train_poly = np.column_stack((train_input **2, train_input))\n","test_poly = np.column_stack((test_input ** 2, test_input))\n","\n","print(train_poly.shape, test_poly.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9FmjnR0B8Qv4","executionInfo":{"status":"ok","timestamp":1728035581348,"user_tz":-540,"elapsed":448,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"4bdd45fc-c588-4d5a-863c-e4b2d1581e0d"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["(42, 2) (14, 2)\n"]}]},{"cell_type":"code","source":["lr = LinearRegression()\n","lr.fit(train_poly, train_target)\n","\n","print(lr.predict([[50**2, 50]]))\n","\n","print(lr.coef_, lr.intercept_)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uctqkdUM8mm9","executionInfo":{"status":"ok","timestamp":1728035808722,"user_tz":-540,"elapsed":393,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"853729f0-f033-48c2-d90d-c5b72aa55139"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["[1573.98423528]\n","[  1.01433211 -21.55792498] 116.0502107827827\n"]}]},{"cell_type":"code","source":["# 구간별 직선을 그리기 위하여 15부터 49까지의 정수 배열을 만듬\n","point = np.arange(15, 50)\n","\n","# 훈련 세트의 산점도 그림\n","plt.scatter(train_input, train_target)\n","\n","# 15에서 49까지 2차 방정식 그래프를 그림\n","plt.plot(point, 1.01*point**2 - 21.6*point + 116.05)\n","\n","# 50cm 농어 데이터\n","plt.scatter([50], [1574], marker='^')\n","plt.xlabel('length')\n","plt.ylabel('weight')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"0zDCaXHx9Usv","executionInfo":{"status":"ok","timestamp":1728036004892,"user_tz":-540,"elapsed":520,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"f2e265ba-e000-4b58-8212-85beaf956c4d"},"execution_count":29,"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":["print(lr.score(train_poly, train_target))\n","print(lr.score(test_poly, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7qLIq6fi-LT8","executionInfo":{"status":"ok","timestamp":1728036019571,"user_tz":-540,"elapsed":407,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"dfccdfc6-48bc-47f9-d851-50afcf164bf1"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9706807451768623\n","0.9775935108325122\n"]}]}]}
\ No newline at end of file
diff --git "a/week2/[MLNovice]\354\241\260\354\230\210\354\235\270_week2-3.ipynb" "b/week2/[MLNovice]\354\241\260\354\230\210\354\235\270_week2-3.ipynb"
new file mode 100644
index 0000000..3095e48
--- /dev/null
+++ "b/week2/[MLNovice]\354\241\260\354\230\210\354\235\270_week2-3.ipynb"
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyO3LO15/hJe+D1s1YJgOOi1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# 특성 공학 : 기존 특성을 사용하여 새로운 특성을 뽑아내는 것"],"metadata":{"id":"tk0g_0Jm-nSv"}},{"cell_type":"markdown","source":["판다스 : 데이터분석 라이브러리 , 데이터 프레임은 핵심적인 구조."],"metadata":{"id":"x9a1XcJb-zTS"}},{"cell_type":"code","source":["import pandas as pd # pd는 판다스의 별칭\n","df = pd.read_csv('https://bit.ly/perch_csv_data')\n","perch_full=df.to_numpy()\n","print(perch_full)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"06ic2nuQ-3TG","executionInfo":{"status":"ok","timestamp":1728036238322,"user_tz":-540,"elapsed":2321,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"ca80004f-71dc-49c6-f39a-9771e11ab46d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 8.4   2.11  1.41]\n"," [13.7   3.53  2.  ]\n"," [15.    3.82  2.43]\n"," [16.2   4.59  2.63]\n"," [17.4   4.59  2.94]\n"," [18.    5.22  3.32]\n"," [18.7   5.2   3.12]\n"," [19.    5.64  3.05]\n"," [19.6   5.14  3.04]\n"," [20.    5.08  2.77]\n"," [21.    5.69  3.56]\n"," [21.    5.92  3.31]\n"," [21.    5.69  3.67]\n"," [21.3   6.38  3.53]\n"," [22.    6.11  3.41]\n"," [22.    5.64  3.52]\n"," [22.    6.11  3.52]\n"," [22.    5.88  3.52]\n"," [22.    5.52  4.  ]\n"," [22.5   5.86  3.62]\n"," [22.5   6.79  3.62]\n"," [22.7   5.95  3.63]\n"," [23.    5.22  3.63]\n"," [23.5   6.28  3.72]\n"," [24.    7.29  3.72]\n"," [24.    6.38  3.82]\n"," [24.6   6.73  4.17]\n"," [25.    6.44  3.68]\n"," [25.6   6.56  4.24]\n"," [26.5   7.17  4.14]\n"," [27.3   8.32  5.14]\n"," [27.5   7.17  4.34]\n"," [27.5   7.05  4.34]\n"," [27.5   7.28  4.57]\n"," [28.    7.82  4.2 ]\n"," [28.7   7.59  4.64]\n"," [30.    7.62  4.77]\n"," [32.8  10.03  6.02]\n"," [34.5  10.26  6.39]\n"," [35.   11.49  7.8 ]\n"," [36.5  10.88  6.86]\n"," [36.   10.61  6.74]\n"," [37.   10.84  6.26]\n"," [37.   10.57  6.37]\n"," [39.   11.14  7.49]\n"," [39.   11.14  6.  ]\n"," [39.   12.43  7.35]\n"," [40.   11.93  7.11]\n"," [40.   11.73  7.22]\n"," [40.   12.38  7.46]\n"," [40.   11.14  6.63]\n"," [42.   12.8   6.87]\n"," [43.   11.93  7.28]\n"," [43.   12.51  7.42]\n"," [43.5  12.6   8.14]\n"," [44.   12.49  7.6 ]]\n"]}]},{"cell_type":"code","source":["import numpy as np\n","perch_weight = np.array([5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0, 110.0,\n","       115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0, 130.0,\n","       150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0, 197.0,\n","       218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0, 514.0,\n","       556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0, 820.0,\n","       850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0, 1000.0,\n","       1000.0])\n"],"metadata":{"id":"QlMhvATK_EgQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cRKkoWoZ9J0m"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","train_input, test_input, train_target, test_target = train_test_split(perch_full, perch_weight, random_state=42)"]},{"cell_type":"markdown","source":["# 사이킷런 변환기"],"metadata":{"id":"Lq-9MiYDDPP-"}},{"cell_type":"code","source":["from sklearn.preprocessing import PolynomialFeatures\n","\n","# transform 전에 꼭 poly.fit 을 사용해야함. 두개를 하나로 합친 fit_transfrom 메서드도 존재.\n","poly = PolynomialFeatures(include_bias=False)\n","poly.fit([[2, 3]])\n","print(poly.transform([[2, 3]]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gwXkfLKbDD_E","executionInfo":{"status":"ok","timestamp":1728037341779,"user_tz":-540,"elapsed":556,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"4fc26d00-dc10-4015-b020-8a91fca8ae2a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[2. 3. 4. 6. 9.]]\n"]}]},{"cell_type":"code","source":["poly = PolynomialFeatures(include_bias=False)\n","# PolynomialFeatures 클래스는 특성이 어떻게 만들어졌는지 확인.\n","\n","poly.fit(train_input)\n","train_poly = poly.transform(train_input)\n","\n","print(train_poly.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aThodBEGDUWN","executionInfo":{"status":"ok","timestamp":1728037546859,"user_tz":-540,"elapsed":540,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"0409f8de-4e2d-416f-8a7f-544d954c43de"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(42, 9)\n"]}]},{"cell_type":"code","source":["poly.get_feature_names_out()\n","\n","test_poly = poly.transform(test_input)"],"metadata":{"id":"Q87AfzpQEGLo"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 다중 회귀 모델 훈련하기"],"metadata":{"id":"54epBE50EXNd"}},{"cell_type":"code","source":["from sklearn.linear_model import LinearRegression\n","\n","lr = LinearRegression()\n","lr.fit(train_poly, train_target)\n","print(lr.score(train_poly, train_target))\n","\n","print(lr.score(test_poly, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ANAScn-OEVFt","executionInfo":{"status":"ok","timestamp":1728037642334,"user_tz":-540,"elapsed":513,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"595b5485-c018-474b-f52a-babd5aaccf62"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9903183436982125\n","0.9714559911594111\n"]}]},{"cell_type":"code","source":["poly = PolynomialFeatures(degree=5, include_bias=False)\n","\n","poly.fit(train_input)\n","train_poly = poly.transform(train_input)\n","test_poly = poly.transform(test_input)\n","\n","print(train_poly.shape)\n","\n","lr.fit(train_poly, train_target)\n","print(lr.score(train_poly, train_target))\n","\n","print(lr.score(test_poly, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tv5HQPCEEbpr","executionInfo":{"status":"ok","timestamp":1728037791163,"user_tz":-540,"elapsed":505,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"956b2785-55a2-4265-8392-fdb203363230"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(42, 55)\n","0.9999999999996433\n","-144.40579436844948\n"]}]},{"cell_type":"markdown","source":["# 규제 : 머신러닝이 훈련 세트를 너무 과도하게 학습하지 못하게 하는 것"],"metadata":{"id":"R0iaradRFGIY"}},{"cell_type":"code","source":["from sklearn.preprocessing import StandardScaler\n","\n","ss = StandardScaler()\n","ss.fit(train_poly)\n","\n","train_scaled = ss.transform(train_poly)\n","test_scaled = ss.transform(test_poly)"],"metadata":{"id":"stQDfVs4FJRs"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 규제 + 선형회귀 = 릿지, 리쏘"],"metadata":{"id":"SPNspg3kFTmU"}},{"cell_type":"markdown","source":["# 릿지 회귀"],"metadata":{"id":"M9-sqOGKFafZ"}},{"cell_type":"code","source":["from sklearn.linear_model import Ridge\n","\n","ridge = Ridge()\n","ridge.fit(train_scaled, train_target)\n","print(ridge.score(train_scaled, train_target))\n","\n","print(ridge.score(test_scaled, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_7E02ysBFcg4","executionInfo":{"status":"ok","timestamp":1728037913657,"user_tz":-540,"elapsed":532,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"9b18a417-a083-414b-9f23-1f613a8a3b27"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9896101671037343\n","0.9790693977615387\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","train_score = []\n","test_score = []\n","\n","alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n","for alpha in alpha_list:\n","    # 릿지 모델\n","    ridge = Ridge(alpha=alpha)\n","    # 릿지 모델 훈련\n","    ridge.fit(train_scaled, train_target)\n","    # 훈련 점수와 테스트 점수를 저장\n","    train_score.append(ridge.score(train_scaled, train_target))\n","    test_score.append(ridge.score(test_scaled, test_target))"],"metadata":{"id":"RNiX5A01FhTm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot(np.log10(alpha_list), train_score)\n","plt.plot(np.log10(alpha_list), test_score)\n","plt.xlabel('alpha')\n","plt.ylabel('R^2')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"lawY9oAxFk80","executionInfo":{"status":"ok","timestamp":1728037940441,"user_tz":-540,"elapsed":1318,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"8d093d87-a997-40a3-8ae1-2d8e492b9035"},"execution_count":null,"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":["ridge = Ridge(alpha=0.1)\n","ridge.fit(train_scaled, train_target)\n","\n","print(ridge.score(train_scaled, train_target))\n","print(ridge.score(test_scaled, test_target))"],"metadata":{"id":"tHnYXISOFmIX"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 라쏘"],"metadata":{"id":"OfNUGZXGFwoP"}},{"cell_type":"code","source":["from sklearn.linear_model import Lasso\n","\n","lasso = Lasso()\n","lasso.fit(train_scaled, train_target)\n","print(lasso.score(train_scaled, train_target))\n","\n","print(lasso.score(test_scaled, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CNIg8lf3FyCV","executionInfo":{"status":"ok","timestamp":1728037994017,"user_tz":-540,"elapsed":523,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"1e5b6254-8ec0-40af-bdce-3389ac81b42e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.989789897208096\n","0.9800593698421883\n"]}]},{"cell_type":"code","source":["train_score = []\n","test_score = []\n","\n","alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n","for alpha in alpha_list:\n","    # 라쏘 모델\n","    lasso = Lasso(alpha=alpha, max_iter=10000)\n","    # 라쏘 모델 훈련\n","    lasso.fit(train_scaled, train_target)\n","    # 훈련 점수와 테스트 점수를 저장\n","    train_score.append(lasso.score(train_scaled, train_target))\n","    test_score.append(lasso.score(test_scaled, test_target))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YDe6fP1HFzlt","executionInfo":{"status":"ok","timestamp":1728038009937,"user_tz":-540,"elapsed":518,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"de97fefb-aa87-4458-e5b9-43f293a8b2e1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+04, tolerance: 5.183e+02\n","  model = cd_fast.enet_coordinate_descent(\n","/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.297e+04, tolerance: 5.183e+02\n","  model = cd_fast.enet_coordinate_descent(\n"]}]},{"cell_type":"code","source":["plt.plot(np.log10(alpha_list), train_score)\n","plt.plot(np.log10(alpha_list), test_score)\n","plt.xlabel('alpha')\n","plt.ylabel('R^2')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"aQgq5iB9F3e3","executionInfo":{"status":"ok","timestamp":1728038016444,"user_tz":-540,"elapsed":605,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"e254187b-8193-4db4-f73f-dbf79797fa60"},"execution_count":null,"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":["lasso = Lasso(alpha=10)\n","lasso.fit(train_scaled, train_target)\n","\n","print(lasso.score(train_scaled, train_target))\n","print(lasso.score(test_scaled, test_target))\n","\n","print(np.sum(lasso.coef_ == 0))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5rQEV_oAF6ED","executionInfo":{"status":"ok","timestamp":1728038026155,"user_tz":-540,"elapsed":3,"user":{"displayName":"조예인","userId":"17650117334011908449"}},"outputId":"e284b214-e7f0-463c-cb78-9ba6c9666913"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9888067471131867\n","0.9824470598706695\n","40\n"]}]}]}
\ No newline at end of file
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