sklearn學習筆記之決策樹分類和線性回歸

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))

# -*- 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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