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- from sklearn.tree import DecisionTreeClassifier
- from sklearn.ensemble import RandomForestClassifier
- from sklearn.datasets import load_wine
- from sklearn.model_selection import train_test_split
- x_train,x_test,y_train,y_test=train_test_split(wine.data,wine.target,test_size=0.3)
- clf=DecisionTreeClassifier(random_state=0)
- rfc=RandomForestClassifier(random_state=0)
- clf=clf.fit(x_train,y_train)
- rfc=rfc.fit(x_train,y_train)
- score_c=clf.score(x_test,y_test)
- score_r=rfc.score(x_test,y_test)
- print(score_c,score_r)
运行结果:
0.8703703703703703 0.9259259259259259
- from sklearn.tree import DecisionTreeClassifier
- from sklearn.ensemble import RandomForestClassifier
- from sklearn.datasets import load_wine
- from sklearn.model_selection import cross_val_score
- import matplotlib.pyplot as plt
- %matplotlib inline
- wine=load_wine()
- rfc=RandomForestClassifier(n_estimators=25)
- rfc_s=cross_val_score(rfc,wine.data,wine.target,cv=10)
- clf=DecisionTreeClassifier()
- clf_s=cross_val_score(clf,wine.data,wine.target,cv=10)
- plt.plot(range(1,11),rfc_s,label='RandomForest')
- plt.plot(range(1,11),clf_s,label='DecisionTree')
- plt.legend()
- plt.show()
运行结果:
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