import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap

%matplotlib notebook

plt.rcParams['font.sans-serif'] = ['PingFang HK'] # 选择一个本地的支持中文的字体 fig, ax = plt.subplots() ax.set_facecolor('#f8f9fa')

获取数据范围

x_min, x_max = X.iloc[:, 0].min() - .5, X.iloc[:, 0].max() + .5 y_min, y_max = X.iloc[:, 1].min() - .5, X.iloc[:, 1].max() + .5

生成网格点坐标

xx, yy = np.meshgrid(np.arange(x_min, x_max, .05), np.arange(y_min, y_max, .05))

对网格点进行预测

Z = rf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape)

设置颜色映射

colors = ['#ffadad', '#8ecae6'] cmap = ListedColormap(colors)

绘制等高线图

plt.contourf(xx, yy, Z, cmap=cmap, alpha=0.3)

绘制散点图

x1 = X[y==-1]['特征1'] y1 = X[y==-1]['特征2'] x2 = X[y==1]['特征1'] y2 = X[y==1]['特征2'] p1 = plt.scatter(x1, y1, c='#e63946', marker='o', s=20) p2 = plt.scatter(x2, y2, c='#457b9d', marker='x', s=20)

设置图例和标题

ax.set_title('随机森林分类', color='#264653') ax.set_xlabel('特征1', color='#264653') ax.set_ylabel('特征2', color='#264653') ax.tick_params(labelcolor='#264653') plt.legend([p1, p2], ["-1", "1"], loc="upper left")

plt.show(