代码笔记
%matplotlib inline from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_wine wine = load_wine() from sklearn.model_selection import train_test_split Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data, wine.target, test_size = 0.3) clf = DecisionTreeClassifier(random_state = 0) rfc = RandomForestClassifier(random_state = 0) clf = clf.fit(Xtrain, Ytrain) rfc = rfc.fit(Xtrain, Ytrain) score_c = clf.score(Xtest, Ytest) score_r = rfc.score(Xtest, Ytest) print("Single Tree:{}".format(score_c), "Random Forest:{}".format(score_r))引入交叉验证评估效果
from sklearn.model_selection import cross_val_score import matplotlib.pyplot as plt 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()调参
#n_estimators 能建多少颗树，越多，效果越好，但是计算量也跟着变大，这个数是调参的重点 rfc_l = [] clf_l = [] for i in range(10): rfc = RandomForestClassifier(n_estimators=25) rfc_s = cross_val_score(rfc, wine.data, wine.target,cv=10).mean() rfc_l.append(rfc_s) clf = DecisionTreeClassifier() clf_s = cross_val_score(clf, wine.data, wine.target,cv=10).mean() clf_l.append(clf_s) plt.plot(range(1,11), rfc_l, label="RandomForest") plt.plot(range(1,11), clf_l, label="DecisionTree") plt.legend() plt.show() superpa = [] for i in range(200): rfc = RandomForestClassifier(n_estimators=i+1, n_jobs=-1) rfc_s = cross_val_score(rfc, wine.data, wine.target, cv=10).mean() superpa.append(rfc_s) print(max(superpa), superpa.index(max(superpa))) plt.figure(figsize=[20,5]) plt.plot(range(1,201),superpa) plt.show() rfc = RandomForestClassifier(n_estimators=25) rfc = rfc.fit(Xtrain, Ytrain) rfc.estimators_ rfc.oob_score_ rfc.feature_importances_ rfc.apply(Xtest) rfc.predict(Xtest) rfc.predict_proba(Xtest)
