决策树进阶：随机森林模型

 1 from sklearn import tree,datasets
 2 from sklearn.model_selection import train_test_split
 3 wine=datasets.load_wine()
 4 X,y=wine.data[:,:2],wine.target
 5 X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=3)
 6 
 7 from sklearn.ensemble import RandomForestClassifier
 8 forest=RandomForestClassifier(n_estimators=6,random_state=3)
 9 forest.fit(X_train,y_train)
10 print("the score of this model:{}".format(forest.score(X_test,y_test)))

 1 from matplotlib.colors import ListedColormap
 2 cmap_light=ListedColormap([‘#FFAAAA‘,‘#AAFFAA‘,‘#AAAAFF‘])
 3 cmap_bold=ListedColormap([‘#FF0000‘,‘#00FF00‘,‘#0000FF‘])
 4 
 5 import numpy as np
 6 from matplotlib import pyplot as plt
 7 x_min=X_train[:,0].min()-1
 8 x_max=X_train[:,0].max()+1
 9 y_min=X_train[:,1].min()-1
10 y_max=X_train[:,1].max()+1
11 xx,yy=np.meshgrid(np.arange(x_min,x_max,.02),
12                   np.arange(y_min,y_max,.02))
13 z=forest.predict(np.c_[xx.ravel(),yy.ravel()])
14 z=z.reshape(xx.shape)
15 plt.figure()
16 plt.pcolormesh(xx,yy,z,cmap=cmap_light)
17 #plt.pcolormesh(xx,yy,z,cmap=plt.cm.Pastel1)
18 plt.scatter(X[:,0],X[:,1],c=y,cmap=cmap_bold,edgecolors=‘k‘,s=20)
19 plt.xlim(xx.min(),xx.max())
20 plt.ylim(yy.min(),yy.max())
21 plt.title("Random Forest(n_estimators=6)")
22 plt.show()