二、sklearn实现线性回归

1、简单线性回归概念

简单线性回归通过拟合线性方程y=wx+b得到预测值，通过取得预测值和真实值的最小差距，得到w和b的值。

　　公式：J(w,b)_min=Σ(yⁱ-yⁱ_pre)²=∑(yⁱ-wxⁱ+b)²，即公式取最小值

2、通过最小二乘法求解w和b

• w = ∑(xⁱ-x_mean)(yⁱ-y_mean)/∑*(xⁱ-x_mean)
• b = y_mean-ax_mean
• 向量化公式：w = XY/XX

import numpy as np
from sklearn import datasets

from sklearn.model_selection import train_test_split

# 导入线性回归库
from sklearn.linear_model import LinearRegression

boston = datasets.load_boston()

a = boston.data
y = boston.target
a = a[y<50.0]
y = y[y<50.0]

X_train, X_test, y_train, y_test = train_test_split(a, y, random_state=666)

lin_reg = LinearRegression()

lin_reg.fit(X_train, y_train)

# 权重系数
lin_reg.coef_

# 截距
lin_reg.intercept_

# R2准确率
lin_reg.score

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