机器学习小总结

【#第一文档网# 导语】以下是®第一文档网的小编为您整理的《机器学习小总结》，欢迎阅读！

pipe = Pipeline([

('mms', MinMaxScaler()), ('skb', SelectKBest(chi2)), ('pca', PCA()),

('decision', DecisionTreeClassifier()) ])

# 参数

parameters = {

"skb__k": [2,3,4],

"pca__n_components": [0.5,1.0],

"decision__criterion": ["gini", "entropy"],

"decision__max_depth": [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15] }

x_train2, x_test2, y_train2, y_test2 = x_train1, x_test1, y_train1, y_test1

gscv = GridSearchCV(pipe, param_grid=parameters) gscv.fit(x_train2, y_train2)

算隐藏参数，最大似然，最大后验概率，em

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