基于随机森林的红酒分类，画图结果无散点，是哪里错了？

Question

import numpy as np
import pandas as pd
from sklearn import datasets

#载入红酒数据
wine = datasets.load_wine()

#只选取前两个特征
X = wine.data[:, :2]
y = wine.target
#转换为二分类
y = y==2
features = ["x1", "x2"]
label = "y"
data = pd.DataFrame(X, columns=features)
data[label] = y

data.head()

#拆分训练集和数据集
from sklearn.model_selection import train_test_split

train_data, test_data = train_test_split(data, test_size=0.5)

#随机森林拟合
from sklearn.ensemble import RandomForestClassifier

forest = RandomForestClassifier(n_estimators=6, random_state=3)
forest.fit(train_data[features], train_data[label])

#绘制图形
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

#定义图像中分区的颜色和散点的颜色
cmap_light= ListedColormap(['#FFAAAA','#AAFFAA','#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000','#00FF00','#0000FF'])

#分布用样本的两个特征值创建图像和横轴和纵轴
x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
y_min, y_max = X[:,0].min()-1, X[:,0].max()+1
#用不同背景色表示不同类
xx, yy =np.meshgrid(np.arange(x_min, x_max, .02),
                    np.arange(y_min, y_max, .02))
z = forest.predict(np.c_[(xx.ravel(), yy.ravel())]).reshape(xx.shape)

plt.figure()
#用颜色标注预测结果
plt.pcolormesh(xx ,yy ,z, cmap=cmap_light)
#用散点把样本标出来
plt.scatter(X[:, 0], X[:, 1], c=y ,cmap=cmap_bold, edgecolors='k', s=20)

plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())

plt.title('Classifier: RandomForestClassifier')#依照参数值修改标题
plt.show()

Answer 1

不知道