ML之Sklearn：利用八种ML算法对根据糖尿病数据集预测新个体是否患糖尿病(利用糖尿病数据集中的八个参数特征预测一个0或1结果)

ML之Sklearn：利用八种ML算法对根据糖尿病数据集预测新个体是否患糖尿病(利用糖尿病数据集中的八个参数特征预测一个0或1结果)



1、预测

 

2、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

3、LoR

# 
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

8、NN

利用多层神经网络




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

全部代码稍后公布！有任何算法理解问题，可留言共同探讨！

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