random_forest_classification.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"machine_shape":"hm","authorship_tag":"ABX9TyPZ03D3zVqvhwuutN0FRclN"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"0MRC0e0KhQ0S"},"source":["# Random Forest Classification"]},{"cell_type":"markdown","metadata":{"id":"LWd1UlMnhT2s"},"source":["## Importing the libraries"]},{"cell_type":"code","metadata":{"id":"YvGPUQaHhXfL","executionInfo":{"status":"ok","timestamp":1667113596879,"user_tz":-240,"elapsed":6,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"K1VMqkGvhc3-"},"source":["## Importing the dataset"]},{"cell_type":"code","metadata":{"id":"M52QDmyzhh9s","executionInfo":{"status":"ok","timestamp":1667113596879,"user_tz":-240,"elapsed":5,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["dataset = pd.read_csv('Social_Network_Ads.csv')\n","X = dataset.iloc[:, :-1].values\n","y = dataset.iloc[:, -1].values"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YvxIPVyMhmKp"},"source":["## Splitting the dataset into the Training set and Test set"]},{"cell_type":"code","metadata":{"id":"AVzJWAXIhxoC","executionInfo":{"status":"ok","timestamp":1667113597775,"user_tz":-240,"elapsed":900,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"P3nS3-6r1i2B","outputId":"a34dbe95-974b-4560-e53c-c38f743166b7","executionInfo":{"status":"ok","timestamp":1667113597776,"user_tz":-240,"elapsed":40,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(X_train)"],"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["[[    44  39000]\n"," [    32 120000]\n"," [    38  50000]\n"," [    32 135000]\n"," [    52  21000]\n"," [    53 104000]\n"," [    39  42000]\n"," [    38  61000]\n"," [    36  50000]\n"," [    36  63000]\n"," [    35  25000]\n"," [    35  50000]\n"," [    42  73000]\n"," [    47  49000]\n"," [    59  29000]\n"," [    49  65000]\n"," [    45 131000]\n"," [    31  89000]\n"," [    46  82000]\n"," [    47  51000]\n"," [    26  15000]\n"," [    60 102000]\n"," [    38 112000]\n"," [    40 107000]\n"," [    42  53000]\n"," [    35  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Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(y_train)"],"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["[0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 1\n"," 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1\n"," 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0\n"," 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0\n"," 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0\n"," 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0\n"," 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0\n"," 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1\n"," 0 0 0 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0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0\n"," 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n"," 0 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1]\n"]}]},{"cell_type":"markdown","metadata":{"id":"kW3c7UYih0hT"},"source":["## Feature Scaling"]},{"cell_type":"code","metadata":{"id":"9fQlDPKCh8sc","executionInfo":{"status":"ok","timestamp":1667113597779,"user_tz":-240,"elapsed":30,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["from sklearn.preprocessing import StandardScaler\n","sc = StandardScaler()\n","X_train = sc.fit_transform(X_train)\n","X_test = sc.transform(X_test)"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"syrnD1Op2BSR","outputId":"35e3de04-4fc7-497c-baf1-9ddf159a03c0","executionInfo":{"status":"ok","timestamp":1667113597780,"user_tz":-240,"elapsed":30,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(X_train)"],"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 0.58164944 -0.88670699]\n"," [-0.60673761  1.46173768]\n"," [-0.01254409 -0.5677824 ]\n"," [-0.60673761  1.89663484]\n"," [ 1.37390747 -1.40858358]\n"," [ 1.47293972  0.99784738]\n"," [ 0.08648817 -0.79972756]\n"," [-0.01254409 -0.24885782]\n"," [-0.21060859 -0.5677824 ]\n"," [-0.21060859 -0.19087153]\n"," [-0.30964085 -1.29261101]\n"," [-0.30964085 -0.5677824 ]\n"," [ 0.38358493  0.09905991]\n"," [ 0.8787462  -0.59677555]\n"," [ 2.06713324 -1.17663843]\n"," [ 1.07681071 -0.13288524]\n"," [ 0.68068169  1.78066227]\n"," [-0.70576986  0.56295021]\n"," [ 0.77971394  0.35999821]\n"," [ 0.8787462  -0.53878926]\n"," [-1.20093113 -1.58254245]\n"," [ 2.1661655   0.93986109]\n"," [-0.01254409  1.22979253]\n"," [ 0.18552042  1.08482681]\n"," [ 0.38358493 -0.48080297]\n"," [-0.30964085 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-0.24885782]\n"," [-0.80480212  0.56295021]\n"," [-1.20093113 -1.5535493 ]\n"," [-0.50770535 -1.11865214]\n"," [ 0.28455268  0.07006676]\n"," [-0.21060859 -1.06066585]\n"," [ 1.67100423  1.6067034 ]\n"," [ 0.97777845  1.78066227]\n"," [ 0.28455268  0.04107362]\n"," [-0.80480212 -0.21986468]\n"," [-0.11157634  0.07006676]\n"," [ 0.28455268 -0.19087153]\n"," [ 1.96810099 -0.65476184]\n"," [-0.80480212  1.3457651 ]\n"," [-1.79512465 -0.59677555]\n"," [-0.11157634  0.12805305]\n"," [ 0.28455268 -0.30684411]\n"," [ 1.07681071  0.56295021]\n"," [-1.00286662  0.27301877]\n"," [ 1.47293972  0.35999821]\n"," [ 0.18552042 -0.3648304 ]\n"," [ 2.1661655  -1.03167271]\n"," [-0.30964085  1.11381995]\n"," [-1.6960924   0.07006676]\n"," [-0.01254409  0.04107362]\n"," [ 0.08648817  1.05583366]\n"," [-0.11157634 -0.3648304 ]\n"," [-1.20093113  0.07006676]\n"," [-0.30964085 -1.3505973 ]\n"," [ 1.57197197  1.11381995]\n"," [-0.80480212 -1.52455616]\n"," [ 0.08648817  1.8676417 ]\n"," [-0.90383437 -0.77073441]\n"," [-0.50770535 -0.77073441]\n"," [-0.30964085 -0.91570013]\n"," [ 0.28455268 -0.71274813]\n"," [ 0.28455268  0.07006676]\n"," [ 0.08648817  1.8676417 ]\n"," [-1.10189888  1.95462113]\n"," [-1.6960924  -1.5535493 ]\n"," [-1.20093113 -1.089659  ]\n"," [-0.70576986 -0.1038921 ]\n"," [ 0.08648817  0.09905991]\n"," [ 0.28455268  0.27301877]\n"," [ 0.8787462  -0.5677824 ]\n"," [ 0.28455268 -1.14764529]\n"," [-0.11157634  0.67892279]\n"," [ 2.1661655  -0.68375498]\n"," [-1.29996338 -1.37959044]\n"," [-1.00286662 -0.94469328]\n"," [-0.01254409 -0.42281668]\n"," [-0.21060859 -0.45180983]\n"," [-1.79512465 -0.97368642]\n"," [ 1.77003648  0.99784738]\n"," [ 0.18552042 -0.3648304 ]\n"," [ 0.38358493  1.11381995]\n"," [-1.79512465 -1.3505973 ]\n"," [ 0.18552042 -0.13288524]\n"," [ 0.8787462  -1.43757673]\n"," [-1.99318916  0.47597078]\n"," [-0.30964085  0.27301877]\n"," [ 1.86906873 -1.06066585]\n"," [-0.4086731   0.07006676]\n"," [ 1.07681071 -0.88670699]\n"," [-1.10189888 -1.11865214]\n"," [-1.89415691  0.01208048]\n"," [ 0.08648817  0.27301877]\n"," [-1.20093113  0.33100506]\n"," [-1.29996338  0.30201192]\n"," [-1.00286662  0.44697764]\n"," [ 1.67100423 -0.88670699]\n"," [ 1.17584296  0.53395707]\n"," [ 1.07681071  0.53395707]\n"," [ 1.37390747  2.331532  ]\n"," [-0.30964085 -0.13288524]\n"," [ 0.38358493 -0.45180983]\n"," [-0.4086731  -0.77073441]\n"," [-0.11157634 -0.50979612]\n"," [ 0.97777845 -1.14764529]\n"," [-0.90383437 -0.77073441]\n"," [-0.21060859 -0.50979612]\n"," [-1.10189888 -0.45180983]\n"," [-1.20093113  1.40375139]]\n"]}]},{"cell_type":"code","metadata":{"id":"JUd6iBRp2C3L","outputId":"c3e1ddb4-46b9-481c-a436-246844604c45","executionInfo":{"status":"ok","timestamp":1667113597780,"user_tz":-240,"elapsed":28,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(X_test)"],"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["[[-0.80480212  0.50496393]\n"," [-0.01254409 -0.5677824 ]\n"," [-0.30964085  0.1570462 ]\n"," [-0.80480212  0.27301877]\n"," [-0.30964085 -0.5677824 ]\n"," [-1.10189888 -1.43757673]\n"," [-0.70576986 -1.58254245]\n"," [-0.21060859  2.15757314]\n"," [-1.99318916 -0.04590581]\n"," [ 0.8787462  -0.77073441]\n"," [-0.80480212 -0.59677555]\n"," [-1.00286662 -0.42281668]\n"," [-0.11157634 -0.42281668]\n"," [ 0.08648817  0.21503249]\n"," [-1.79512465  0.47597078]\n"," [-0.60673761  1.37475825]\n"," [-0.11157634  0.21503249]\n"," [-1.89415691  0.44697764]\n"," [ 1.67100423  1.75166912]\n"," [-0.30964085 -1.37959044]\n"," [-0.30964085 -0.65476184]\n"," [ 0.8787462   2.15757314]\n"," [ 0.28455268 -0.53878926]\n"," [ 0.8787462   1.02684052]\n"," [-1.49802789 -1.20563157]\n"," [ 1.07681071  2.07059371]\n"," [-1.00286662  0.50496393]\n"," [-0.90383437  0.30201192]\n"," [-0.11157634 -0.21986468]\n"," [-0.60673761  0.47597078]\n"," [-1.6960924   0.53395707]\n"," [-0.11157634  0.27301877]\n"," [ 1.86906873 -0.27785096]\n"," [-0.11157634 -0.48080297]\n"," [-1.39899564 -0.33583725]\n"," [-1.99318916 -0.50979612]\n"," [-1.59706014  0.33100506]\n"," [-0.4086731  -0.77073441]\n"," [-0.70576986 -1.03167271]\n"," [ 1.07681071 -0.97368642]\n"," [-1.10189888  0.53395707]\n"," [ 0.28455268 -0.50979612]\n"," [-1.10189888  0.41798449]\n"," [-0.30964085 -1.43757673]\n"," [ 0.48261718  1.22979253]\n"," [-1.10189888 -0.33583725]\n"," [-0.11157634  0.30201192]\n"," [ 1.37390747  0.59194336]\n"," [-1.20093113 -1.14764529]\n"," [ 1.07681071  0.47597078]\n"," [ 1.86906873  1.51972397]\n"," [-0.4086731  -1.29261101]\n"," [-0.30964085 -0.3648304 ]\n"," [-0.4086731   1.31677196]\n"," [ 2.06713324  0.53395707]\n"," [ 0.68068169 -1.089659  ]\n"," [-0.90383437  0.38899135]\n"," [-1.20093113  0.30201192]\n"," [ 1.07681071 -1.20563157]\n"," [-1.49802789 -1.43757673]\n"," [-0.60673761 -1.49556302]\n"," [ 2.1661655  -0.79972756]\n"," [-1.89415691  0.18603934]\n"," [-0.21060859  0.85288166]\n"," [-1.89415691 -1.26361786]\n"," [ 2.1661655   0.38899135]\n"," [-1.39899564  0.56295021]\n"," [-1.10189888 -0.33583725]\n"," [ 0.18552042 -0.65476184]\n"," [ 0.38358493  0.01208048]\n"," [-0.60673761  2.331532  ]\n"," [-0.30964085  0.21503249]\n"," [-1.59706014 -0.19087153]\n"," [ 0.68068169 -1.37959044]\n"," [-1.10189888  0.56295021]\n"," [-1.99318916  0.35999821]\n"," [ 0.38358493  0.27301877]\n"," [ 0.18552042 -0.27785096]\n"," [ 1.47293972 -1.03167271]\n"," [ 0.8787462   1.08482681]\n"," [ 1.96810099  2.15757314]\n"," [ 2.06713324  0.38899135]\n"," [-1.39899564 -0.42281668]\n"," [-1.20093113 -1.00267957]\n"," [ 1.96810099 -0.91570013]\n"," [ 0.38358493  0.30201192]\n"," [ 0.18552042  0.1570462 ]\n"," [ 2.06713324  1.75166912]\n"," [ 0.77971394 -0.8287207 ]\n"," [ 0.28455268 -0.27785096]\n"," [ 0.38358493 -0.16187839]\n"," [-0.11157634  2.21555943]\n"," [-1.49802789 -0.62576869]\n"," [-1.29996338 -1.06066585]\n"," [-1.39899564  0.41798449]\n"," [-1.10189888  0.76590222]\n"," [-1.49802789 -0.19087153]\n"," [ 0.97777845 -1.06066585]\n"," [ 0.97777845  0.59194336]\n"," [ 0.38358493  0.99784738]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"bb6jCOCQiAmP"},"source":["## Training the Random Forest Classification model on the Training set"]},{"cell_type":"code","metadata":{"id":"e0pFVAmciHQs","outputId":"cfdd39a8-30a7-4940-da8f-253a29eec274","executionInfo":{"status":"ok","timestamp":1667113597781,"user_tz":-240,"elapsed":24,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["from sklearn.ensemble import RandomForestClassifier\n","classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n","classifier.fit(X_train, y_train)"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"yyxW5b395mR2"},"source":["## Predicting a new result"]},{"cell_type":"code","metadata":{"id":"f8YOXsQy58rP","outputId":"12dfca28-b114-4307-dd1d-cac6bcb61ad5","executionInfo":{"status":"ok","timestamp":1667113597782,"user_tz":-240,"elapsed":22,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(classifier.predict(sc.transform([[30,87000]])))"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["[0]\n"]}]},{"cell_type":"markdown","metadata":{"id":"vKYVQH-l5NpE"},"source":["## Predicting the Test set results"]},{"cell_type":"code","metadata":{"id":"p6VMTb2O4hwM","outputId":"850d533e-75a2-472a-ba7a-7ceb8b0cae7f","executionInfo":{"status":"ok","timestamp":1667113597783,"user_tz":-240,"elapsed":21,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["y_pred = classifier.predict(X_test)\n","print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["[[0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [1 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 0]\n"," [1 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 1]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [1 1]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 0]\n"," [1 1]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [1 1]\n"," [1 1]\n"," [1 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 1]\n"," [0 0]\n"," [1 1]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 1]\n"," [0 0]\n"," [1 1]\n"," [1 1]\n"," [1 1]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"h4Hwj34ziWQW"},"source":["## Making the Confusion Matrix"]},{"cell_type":"code","metadata":{"id":"D6bpZwUiiXic","outputId":"9ff6f710-d75d-470b-bc56-6b8c7d6ee150","executionInfo":{"status":"ok","timestamp":1667113597783,"user_tz":-240,"elapsed":19,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["from sklearn.metrics import confusion_matrix, accuracy_score\n","cm = confusion_matrix(y_test, y_pred)\n","print(cm)\n","accuracy_score(y_test, y_pred)"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["[[63  5]\n"," [ 4 28]]\n"]},{"output_type":"execute_result","data":{"text/plain":["0.91"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"6OMC_P0diaoD"},"source":["## Visualising the Training set results"]},{"cell_type":"code","metadata":{"id":"_NOjKvZRid5l","outputId":"49223f23-51fa-40ac-9582-185a063a4651","executionInfo":{"status":"ok","timestamp":1667113677787,"user_tz":-240,"elapsed":80019,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":351}},"source":["from matplotlib.colors import ListedColormap\n","X_set, y_set = sc.inverse_transform(X_train), y_train\n","X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n","                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n","plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n","             alpha = 0.75, cmap = ListedColormap(('salmon', 'dodgerblue')))\n","plt.xlim(X1.min(), X1.max())\n","plt.ylim(X2.min(), X2.max())\n","for i, j in enumerate(np.unique(y_set)):\n","    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('salmon', 'dodgerblue'))(i), label = j)\n","plt.title('Random Forest Classification (Training set)')\n","plt.xlabel('Age')\n","plt.ylabel('Estimated Salary')\n","plt.legend()\n","plt.show()"],"execution_count":15,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n","WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"SZ-j28aPihZx"},"source":["## Visualising the Test set results"]},{"cell_type":"code","metadata":{"id":"qeTjz2vDilAC","outputId":"4e27b9c4-3ddb-419e-bdd7-ade8ab597192","executionInfo":{"status":"ok","timestamp":1667113764835,"user_tz":-240,"elapsed":87052,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":351}},"source":["from matplotlib.colors import ListedColormap\n","X_set, y_set = sc.inverse_transform(X_test), y_test\n","X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n","                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n","plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n","             alpha = 0.75, cmap = ListedColormap(('salmon', 'dodgerblue')))\n","plt.xlim(X1.min(), X1.max())\n","plt.ylim(X2.min(), X2.max())\n","for i, j in enumerate(np.unique(y_set)):\n","    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('salmon', 'dodgerblue'))(i), label = j)\n","plt.title('Random Forest Classification (Test set)')\n","plt.xlabel('Age')\n","plt.ylabel('Estimated Salary')\n","plt.legend()\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n","WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]}]}