(相关资料图)

# 导入包from sklearn.datasets import load_winefrom sklearn.model_selection import train_test_splitfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.ensemble import RandomForestClassifier

# 实例化红酒数据集wine = load_wine()

# 划分测试集和训练集x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3)

# 实例化决策树和随机森林，random_state=0clf = DecisionTreeClassifier(random_state=0)rfc = RandomForestClassifier(random_state=0)

# 训练模型clf.fit(x_train, y_train)rfc.fit(x_train, y_train)

RandomForestClassifier(random_state=0)

RandomForestClassifier(random_state=0)

# 返回测试集的分clf_score = clf.score(x_test, y_test)rfc_score = rfc.score(x_test, y_test)print("sinle tree: {0}\nrandom tree: {1}".format(clf_score, rfc_score))

sinle tree: 0.9074074074074074random tree: 0.9629629629629629

# 导入交叉验证和画图工具%matplotlib inlinefrom sklearn.model_selection import cross_val_scoreimport matplotlib.pyplot as plt

# 实例化决策树和随机森林clf = DecisionTreeClassifier()rfc = RandomForestClassifier(n_estimators=25) #创建25棵树组成的随机森林

# 实例化交叉验证 10次clf_corss = cross_val_score(clf, wine.data, wine.target, cv=10)rfc_corss = cross_val_score(rfc, wine.data, wine.target, cv=10)

# 查看决策树和随机森林的最好结果print("single tree mean socre: {}\nrandom tree mean socre {}".format(clf_corss.mean(), rfc_corss.mean()))

single tree mean socre: 0.8705882352941178random tree mean socre 0.9722222222222221

# 画出决策树和随机森林对比图plt.plot(range(1, 11), clf_corss, label="single tree")plt.plot(range(1, 11), rfc_corss, label="random tree")plt.xticks(range(1, 11))plt.legend()

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clf_corss = cross_val_score(clf, wine.data, wine.target, cv=10)clf_corss

array([0.88888889, 0.88888889, 0.72222222, 0.88888889, 0.83333333,       0.83333333, 1.        , 0.94444444, 0.94117647, 0.76470588])

rfc_corss = cross_val_score(rfc, wine.data, wine.target, cv=10)rfc_corss

array([1.        , 1.        , 0.94444444, 0.94444444, 0.88888889,       1.        , 1.        , 1.        , 1.        , 1.        ])

# 创建分数列表clf_list = []rfc_list = []

for i in range(10):    clf = DecisionTreeClassifier()    rfc = RandomForestClassifier(n_estimators=25)    clf_corss_mean = cross_val_score(clf, wine.data, wine.target, cv=10).mean()    rfc_corss_mean = cross_val_score(rfc, wine.data, wine.target, cv=10).mean()    clf_list.append(clf_corss_mean)    rfc_list.append(rfc_corss_mean)

# 画出决策树和随机森林对比图plt.plot(range(1, 11), clf_list, label="single tree")plt.plot(range(1, 11), rfc_list, label="random tree")plt.xticks(range(1, 11))plt.legend()

# 1-200颗树的学习曲线superpa = []for i in range(200):    rfc = RandomForestClassifier(n_estimators=i+1, n_jobs=-1)    rfc_cross = cross_val_score(rfc, wine.data, wine.target, cv=10).mean()    superpa.append(rfc_cross)print(max(superpa), superpa.index(max(superpa)))plt.figure(figsize=(20,8))plt.plot(range(1,201), superpa, label="rfc_cross_mean")plt.legend()

0.9888888888888889 20

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