用于分类算法的散点图

Question

请帮我创建此分类算法的散点图。这里在y中，我有一列标签( 0，1)，我希望两个标签都有两种不同颜色的预测标签。

X = np.array(df.iloc[: , [0, 1,2,3,4,5,6,7,8,9,10,]].values)
y = df.iloc[: , 17].values 
dtc = DecisionTreeClassifier()
train_x, test_x, train_y, test_y = train_test_split(X, y, train_size = 0.8, shuffle = True)
kf = KFold(n_splits = 5)
dtc=dtc.fit(train_x, train_y)
dtc_labels = dtc.predict(test_x)

Answer 1

我无法访问您的数据帧，但假设我理解正确，这里有一个最小的工作示例。

关键是，在绘图过程中，您必须对numpy数组使用逻辑索引。最后两行举例说明了这一点。

import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split, KFold
import matplotlib.pyplot as plt
X = np.zeros((100,2))
X[:,0] = np.array(list(range(100)))
X[:,1] = np.array(list(range(100)))
y = list([0] * 50 + [1] * 50)
dtc = DecisionTreeClassifier()
train_x, test_x, train_y, test_y = train_test_split(X, y, train_size = 0.8, shuffle = True)
kf = KFold(n_splits = 5)
dtc=dtc.fit(train_x, train_y)
dtc_labels = dtc.predict(test_x)

plt.scatter(test_x[dtc_labels == 0,0],test_x[dtc_labels == 0,1])
plt.scatter(test_x[dtc_labels == 1,0],test_x[dtc_labels == 1,1])