在統(tǒng)計學(xué)中灰蛙，線性回歸（英語：linear regression）是利用稱為線性回歸方程的最小二乘函數(shù)對一個或多個自變量和因變量之間關(guān)系進(jìn)行建模的一種回歸分析。這種函數(shù)是一個或多個稱為回歸系數(shù)的模型參數(shù)的線性組合隔躲。只有一個自變量的情況稱為簡單回歸摩梧，大于一個自變量情況的叫做多元回歸（multivariable linear regression）。
使用scikit-learn實現(xiàn)線性回歸
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from playML.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
###波士頓房價數(shù)據(jù)集
boston = datasets.load_boston()
X = boston.data
y = boston.target
X = X[y < 50.0]
y = y[y < 50.0]
X_train, X_test, y_train, y_test = train_test_split(X, y, seed=666)
lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)
lin_reg.coef_
###Output:array([ -1.18919477e-01, 3.63991462e-02, -3.56494193e-02,
5.66737830e-02, -1.16195486e+01, 3.42022185e+00,
-2.31470282e-02, -1.19509560e+00, 2.59339091e-01,
-1.40112724e-02, -8.36521175e-01, 7.92283639e-03,
-3.81966137e-01])
lin_reg.score(X_test, y_test)
###回歸系數(shù)Output:0.81298026026584758
