ML之分類預測：基于sklearn庫的七八種機器學習算法利用糖尿病(diabetes)數(shù)據(jù)集(8→1)實現(xiàn)二分類預測

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目錄

輸出結果

數(shù)據(jù)集展示

輸出結果

1、k-NN

2、LoR

4、DT

5、RF

6、GB

???7、SVM

8、NN

設計思路

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輸出結果

數(shù)據(jù)集展示

輸出結果

1、k-NN

k-NN：Accuracy of K-NN classifier on training set: 0.79
k-NN：Accuracy of K-NN classifier on test set: 0.78

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2、LoR

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LoR：C1 Training set accuracy: 0.781
LoR：C1 Test set accuracy: 0.771
LoR：C100 Training set accuracy: 0.785
LoR：C100 Test set accuracy: 0.766
LoR：C001 Training set accuracy: 0.700
LoR：C001 Test set accuracy: 0.703

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4、DT

DT：Accuracy on training set: 1.000
DT：Accuracy on test set: 0.714
DT：Accuracy on training set: 0.773
DT：Accuracy on test set: 0.740

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5、RF

RF：Accuracy on training set: 1.000
RF：Accuracy on test set: 0.786
RF：max_depth=3 Accuracy on training set: 0.800
RF：max_depth=3 Accuracy on test set: 0.755

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6、GB

GB：Accuracy on training set: 0.917
GB：Accuracy on test set: 0.792
GB：Accuracy on training set: 0.804
GB：Accuracy on test set: 0.781
GB：Accuracy on training set: 0.802
GB：Accuracy on test set: 0.776

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7、SVM

SVM：Accuracy on training set: 1.00
SVM：Accuracy on test set: 0.65
SVM：MinMaxScaler Accuracy on training set: 0.77
SVM：MinMaxScaler Accuracy on test set: 0.77
SVM：C=500 Accuracy on training set: 0.790
SVM：C=500 Accuracy on test set: 0.792
SVM：C=1000 Accuracy on training set: 0.790
SVM：C=1000 Accuracy on test set: 0.797
SVM：C=2000 Accuracy on training set: 0.800
SVM：C=2000 Accuracy on test set: 0.797

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8、NN

利用多層神經(jīng)網(wǎng)絡

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NN：Data standardization—Accuracy on training set: 0.823
NN：Data standardization—Accuracy on test set: 0.802
NN：Data standardization(max_iter=1000)—Accuracy on training set: 0.877
NN：Data standardization(max_iter=1000)—Accuracy on test set: 0.755
NN：Data standardization(max_iter=1000,alpha=1)—Accuracy on training set: 0.795
NN：Data standardization(max_iter=1000,alpha=1)—Accuracy on test set: 0.792

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設計思路

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相關文章
ML之Classification：基于sklearn庫的七八種機器學習算法利用糖尿病(diabetes)數(shù)據(jù)集(8→1)實現(xiàn)二分類預測

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