机器学习-岭回归实现

【实例简介】

通过岭回归实现 数据标签的预测

【实例截图】

【核心代码】

import numpy as np # 快速操作结构数组的工具
import matplotlib.pyplot as plt # 可视化绘制
from sklearn.linear_model import Ridge, RidgeCV # Ridge岭回归,RidgeCV带有广义交叉验证的岭回归
# 样本数据集，第一列为x，第二列为y，在x和y之间建立回归模型
data = [
[0.067732, 3.176513], [0.427810, 3.816464], [0.995731, 4.550095], [
0.738336, 4.256571], [0.981083, 4.560815],
[0.526171, 3.929515], [0.378887, 3.526170], [0.033859, 3.156393], [
0.132791, 3.110301], [0.138306, 3.149813],
[0.247809, 3.476346], [0.648270, 4.119688], [0.731209, 4.282233], [
0.236833, 3.486582], [0.969788, 4.655492],
[0.607492, 3.965162], [0.358622, 3.514900], [0.147846, 3.125947], [
0.637820, 4.094115], [0.230372, 3.476039],
[0.070237, 3.210610], [0.067154, 3.190612], [0.925577, 4.631504], [
0.717733, 4.295890], [0.015371, 3.085028],
[0.335070, 3.448080], [0.040486, 3.167440], [
0.212575, 3.364266], [0.617218, 3.993482], [0.541196, 3.891471]
]
# 生成X和y矩阵
dataMat = np.array(data)
X = dataMat[:, 0:1] # 变量x
y = dataMat[:, 1] # 变量y
# ========岭回归========
model = Ridge(alpha=0.5)
model = RidgeCV(alphas=[0.1, 1.0, 10.0]) # 通过RidgeCV可以设置多个参数值，算法使用交叉验证获取最佳参数值
model.fit(X, y) # 线性回归建模
print('系数矩阵:\n', model.coef_)
print('线性回归模型:\n', model)
# print('交叉验证最佳alpha值',model.alpha_) # 只有在使用RidgeCV算法时才有效
# 使用模型预测
predicted = model.predict(X)
# 绘制散点图 参数：x横轴 y纵轴
plt.scatter(X, y, marker='x')
plt.plot(X, predicted, c='r')
# 绘制x轴和y轴坐标
plt.xlabel("x")
plt.ylabel("y")
# 显示图形
plt.show()
if __name__ == "__main__":
pass