sklearn學習筆記之決策樹分類和線性回歸

2021-08-18 09:53:42 字數 2776 閱讀 2441

decisoin tree：


# -*- coding: utf-8 -*-


importsklearn


fromsklearnimporttree


importmatplotlib.pyplotasplt


fromsklearn.model_selectionimporttrain_test_split


fromsklearnimportdatasets


importpandasaspd


importnumpy


defgetdata_1():


iris = datasets.load_iris()


x = iris.data   #樣本特徵矩陣，150*4矩陣，每行乙個樣本，每個樣本維度是4


y = iris.target #樣本類別矩陣，150維行向量，每個元素代表乙個樣本的類別


df1=pd.dataframe(x, columns =['sepallengthcm','sepalwidthcm','petallengthcm','petalwidthcm'])


df1['target']=y


returndf1


df=getdata_1()


x_train, x_test, y_train, y_test = train_test_split(df.iloc[:,0:3],df['target'], test_size=0.3, random_state=42)


printx_train, x_test, y_train, y_test


model = tree.decisiontreeclassifier(criterion='gini')   #cart樹


model.fit(x_train, y_train)


model2= tree.decisiontreeclassifier(criterion='entropy')  #c4.5樹


model2.fit(x_train, y_train)


print'cart樹:'.format(model.score(x_test, y_test))   # 決策樹


print'c4.5樹::'.format(model2.score(x_test, y_test))
結果：輸出的準確度






# -*- coding: utf-8 -*-


importsklearn


fromsklearn.datasets.samples_generatorimportmake_classification


fromsklearn.linear_modelimportlinearregression


importmatplotlib.pyplotasplt


fromsklearn.model_selectionimporttrain_test_split


x, y = make_classification(n_samples=2400, n_features=5, n_informative=2,


n_redundant=2, n_classes=2, n_clusters_per_class=2, scale=1.0,


random_state=20)


x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)


model = linearregression(fit_intercept=true, normalize=false,


copy_x=true, n_jobs=1)


model.fit(x_train, y_train)


print'finish'printmodel.score(x_train, y_train) # 線性回歸：r square； 分類問題： acc


printmodel.score(x_test, y_test)


printx_train,y_train


printx_test,y_test
				
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