ML之分类预测：基于sklearn库的七八种机器学习算法利用糖尿病(diabetes)数据集(8→1)实现二分类预测（一）

输出结果

数据集展示

输出结果

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

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

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

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

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

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