{ "cells": [ { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true, "id": "YiqoDvwciAfw" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "#!pip install pycaret\n", "import pycaret as py" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "PzTN5VW4iAf_" }, "outputs": [], "source": [ "from pycaret.classification import *" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "VPOnf5JwiAgC", "outputId": "85111d4b-826b-44f2-e520-28f638cda692" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " gender age hypertension heart_disease ever_married work_type \\\n", "0 Male 67.0 0 1 Yes Private \n", "1 Male 80.0 0 1 Yes Private \n", "2 Female 49.0 0 0 Yes Private \n", "3 Female 79.0 1 0 Yes Self-employed \n", "4 Male 81.0 0 0 Yes Private \n", "\n", " Residence_type avg_glucose_level bmi smoking_status stroke \n", "0 Urban 228.69 36.6 formerly smoked 1 \n", "1 Rural 105.92 32.5 never smoked 1 \n", "2 Urban 171.23 34.4 smokes 1 \n", "3 Rural 174.12 24.0 never smoked 1 \n", "4 Urban 186.21 29.0 formerly smoked 1 " ], "text/html": [ "\n", "
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genderagehypertensionheart_diseaseever_marriedwork_typeResidence_typeavg_glucose_levelbmismoking_statusstroke
0Male67.001YesPrivateUrban228.6936.6formerly smoked1
1Male80.001YesPrivateRural105.9232.5never smoked1
2Female49.000YesPrivateUrban171.2334.4smokes1
3Female79.010YesSelf-employedRural174.1224.0never smoked1
4Male81.000YesPrivateUrban186.2129.0formerly smoked1
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 DescriptionValue
0Session id6614
1Targetstroke
2Target typeBinary
3Original data shape(4981, 11)
4Transformed data shape(8119, 17)
5Transformed train set shape(6624, 17)
6Transformed test set shape(1495, 17)
7Numeric features5
8Categorical features5
9PreprocessTrue
10Imputation typesimple
11Numeric imputationmean
12Categorical imputationmode
13Maximum one-hot encoding25
14Encoding methodNone
15Fix imbalanceTrue
16Fix imbalance methodSMOTE
17NormalizeTrue
18Normalize methodminmax
19Fold GeneratorStratifiedKFold
20Fold Number10
21CPU Jobs-1
22Use GPUFalse
23Log ExperimentFalse
24Experiment Nameclf-default-name
25USIb230
\n" ] }, "metadata": {} } ], "source": [ "# init setup\n", "from pycaret.classification import *\n", "clf1 = setup(data, target = 'stroke', fix_imbalance = True, normalize = True, normalize_method = 'minmax')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 583, "referenced_widgets": [ "d6aec64a520e45c59a1d7ff21afab2a6", "2b6c3c317ee14b83800511533f3c3966", "a5a47acdd787405189ad6ecb364f0260", "88a8e29913d94310b7813dc6fecbabf0", "526f4bd16f5941b7b147b199a4f74ab6", "42ab3a06bae44738adb1cd3a7e063c0c", "5a2c0f4b7ac24a6b9a6a45b22fc6ebde", "02a0003d391a4be79b49b4264a3f56d6", "c7b063eb359749b699c50627962ee14d", "5e7b4a601976495abeb1594a748c1f31", "303bef0ded6a426880f92c1eeb56cb5c" ] }, "id": "bPCM0EYuiAgH", "outputId": "4c94d6fe-1dad-4c5f-d0c4-6ec25412517a" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 ModelAccuracyAUCRecallPrec.F1KappaMCCTT (Sec)
dummyDummy Classifier0.95010.50000.00000.00000.00000.00000.00000.1590
adaAda Boost Classifier0.94580.80910.05200.34500.08790.07370.11260.7550
gbcGradient Boosting Classifier0.94410.81260.02880.18330.04920.03480.05251.5730
rfRandom Forest Classifier0.94260.77790.01760.08330.02870.01380.01761.0440
lightgbmLight Gradient Boosting Machine0.94120.79090.02880.11670.04560.02740.03460.8560
etExtra Trees Classifier0.93720.74680.05750.14390.08010.05580.06230.6030
xgboostExtreme Gradient Boosting0.93550.77230.03950.14650.05980.03430.04570.5770
dtDecision Tree Classifier0.90390.53070.11600.09960.10600.05640.05680.1840
knnK Neighbors Classifier0.84800.61440.19480.07830.11140.04410.05010.2180
lrLogistic Regression0.74760.83820.76440.13780.23310.16230.24910.3130
ldaLinear Discriminant Analysis0.73380.83950.78170.13330.22750.15550.24580.1640
ridgeRidge Classifier0.73320.84030.78730.13390.22860.15670.24790.2290
svmSVM - Linear Kernel0.69990.83040.82250.12620.21800.14390.24110.1800
qdaQuadratic Discriminant Analysis0.65170.78300.74250.10530.18280.10480.18230.1670
nbNaive Bayes0.57280.79790.85070.09350.16810.08600.18090.1660
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DummyClassifier(constant=None, random_state=6614, strategy='prior')
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 AccuracyAUCRecallPrec.F1KappaMCC
Fold       
00.93700.73400.00000.00000.0000-0.0226-0.0273
10.93980.81390.17650.30000.22220.19310.2005
20.93700.77240.00000.00000.0000-0.0191-0.0251
30.94270.88020.05560.25000.09090.07350.0966
40.94560.77640.00000.00000.0000-0.0055-0.0125
50.94560.77210.11110.40000.17390.15500.1899
60.94250.85540.05880.20000.09090.07030.0847
70.94250.81390.05880.20000.09090.07030.0847
80.94540.72740.05880.25000.09520.07810.1006
90.95110.85210.00000.00000.00000.00000.0000
Mean0.94290.79980.05200.16000.07640.05930.0692
Std0.00410.04930.05470.14110.07420.06960.0798
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Initiated. . . . . . . . . . . . . . . . . .20:30:50
Status. . . . . . . . . . . . . . . . . .Searching Hyperparameters
Estimator. . . . . . . . . . . . . . . . . .Ada Boost Classifier
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 AccuracyAUCRecallPrec.F1KappaMCC
Fold       
00.90540.75700.23530.16670.19510.14640.1489
10.93120.87820.35290.31580.33330.29720.2977
20.93120.83380.11110.20000.14290.11010.1153
30.92550.86620.22220.25000.23530.19630.1967
40.91400.79970.27780.22730.25000.20490.2061
50.90260.80870.16670.13640.15000.09890.0994
60.92530.88810.11760.15380.13330.09500.0960
70.91090.84670.11760.11110.11430.06740.0675
80.88790.72830.23530.13330.17020.11500.1204
90.92240.83370.23530.22220.22860.18780.1878
Mean0.91570.82400.20720.19170.19530.15190.1536
Std0.01340.04900.07470.06010.06370.06600.0654
\n" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0,\n", " n_estimators=50, random_state=6614)" ], "text/html": [ "
AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0,\n",
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