1.决策树的缺点
上图，红色圈的部分就是剪枝的部分，进行后剪枝。
2.剪枝
3.随机森林
注意：随机森林所做的修改就是从所有属性中选择k个属性，再从k个属性中选择最佳的分割属性。
4.代码示例
(1)决策树案例
#!/usr/bin/python # -*- coding:utf-8 -*- import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl from sklearn import tree from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline def iris_type(s): it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2} return it[s] # 花萼长度、花萼宽度，花瓣长度，花瓣宽度 # iris_feature = 'sepal length', 'sepal width', 'petal length', 'petal width' iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度' if __name__ == "__main__": mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False path = '8.iris.data' # 数据文件路径 data = np.loadtxt(path, dtype=float, delimiter=',', converters={4: iris_type}) x, y = np.split(data, (4,), axis=1) # 为了可视化，仅使用前两列特征 x = x[:, :2] x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1) #ss = StandardScaler() #ss = ss.fit(x_train) # 决策树参数估计 # min_samples_split = 10：如果该结点包含的样本数目大于10，则(有可能)对其分支 # min_samples_leaf = 10：若将某结点分支后，得到的每个子结点样本数目都大于10，则完成分支；否则，不进行分支 model = Pipeline([ ('ss', StandardScaler()), ('DTC', DecisionTreeClassifier(criterion='entropy', max_depth=3))]) # clf = DecisionTreeClassifier(criterion='entropy', max_depth=3) model = model.fit(x_train, y_train) y_test_hat = model.predict(x_test) # 测试数据 # 保存 # dot -Tpng -o 1.png 1.dot f = open('iris_tree.dot', 'w') tree.export_graphviz(model.get_params('DTC')['DTC'], out_file=f) # 画图 N, M = 100, 100 # 横纵各采样多少个值 x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围 x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围 t1 = np.linspace(x1_min, x1_max, N) t2 = np.linspace(x2_min, x2_max, M) x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点 x_show = np.stack((x1.flat, x2.flat), axis=1) # 测试点 # # 无意义，只是为了凑另外两个维度 # # 打开该注释前，确保注释掉x = x[:, :2] # x3 = np.ones(x1.size) * np.average(x[:, 2]) # x4 = np.ones(x1.size) * np.average(x[:, 3]) # x_test = np.stack((x1.flat, x2.flat, x3, x4), axis=1) # 测试点 cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF']) cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) y_show_hat = model.predict(x_show) # 预测值 y_show_hat = y_show_hat.reshape(x1.shape) # 使之与输入的形状相同 plt.figure(facecolor='w') plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light) # 预测值的显示 plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test.ravel(), edgecolors='k', s=100, cmap=cm_dark, marker='o') # 测试数据 plt.scatter(x[:, 0], x[:, 1], c=y.ravel(), edgecolors='k', s=40, cmap=cm_dark) # 全部数据 plt.xlabel(iris_feature[0], fontsize=15) plt.ylabel(iris_feature[1], fontsize=15) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.grid(True) plt.title(u'鸢尾花数据的决策树分类', fontsize=17) plt.show() # 训练集上的预测结果 y_test = y_test.reshape(-1) print(y_test_hat) print(y_test) result = (y_test_hat == y_test) # True则预测正确，False则预测错误 acc = np.mean(result) print('准确度: %.2f%%' % (100 * acc)) # 过拟合：错误率 depth = np.arange(1, 15) err_list = [] for d in depth: clf = DecisionTreeClassifier(criterion='entropy', max_depth=d) clf = clf.fit(x_train, y_train) y_test_hat = clf.predict(x_test) # 测试数据 result = (y_test_hat == y_test) # True则预测正确，False则预测错误 err = 1 - np.mean(result) err_list.append(err) print(d, ' 准确度: %.2f%%' % (100 * err)) plt.figure(facecolor='w') plt.plot(depth, err_list, 'ro-', lw=2) plt.xlabel(u'决策树深度', fontsize=15) plt.ylabel(u'错误率', fontsize=15) plt.title(u'决策树深度与过拟合', fontsize=17) plt.grid(True) plt.show()
效果图：
