使用KNeighborsRegressor对make_regression数据集进行训练并预测
1.导入make_regression数据集
作者：小白-小天
import matplotlib.pyplot as plt
from sklearn.datasets import make_regression
# 样本特征为1 噪音为50
X, y = make_regression(n_features=1, n_informative=1, noise=50, random_state=8)
plt.scatter(X, y, c='orange', edgecolors='k')
# 画出来之后 横轴为特征值 纵轴为样本测定值
plt.show()
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
reg = KNeighborsRegressor()
reg.fit(X, y)
z = np.linspace(-5, 5, 300).reshape(-1, 1)
plt.plot(z, reg.predict(z), c='k', linewidth=3)
print(reg.score(X, y))
plt.show()
# KNeighborsRegressor的n_neighbors的默认值为5 设置为2试试
reg = KNeighborsRegressor(n_neighbors=2)
reg.fit(X, y)
plt.scatter(X, y, c='orange', edgecolors='k')
z = np.linspace(-5, 5, 300).reshape(-1, 1)
plt.plot(z, reg.predict(z), c='k', linewidth=3)
print(reg.score(X, y))
plt.show()```
作者：小白-小天
