from sklearn.linear_model import LinearRegression
reg = LinearRegression()
# 方法
reg.fit(X,y,sample_weight = None)  #使用X作为训练数据拟合模型，y作为X的类别值。X，y为数组或者矩阵

reg.predict([[X,y]])  # 预测提供的数据对应的结果
 
#属性
reg.coef_   #表示回归系数w=(w1,w2....)

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression, SGDRegressor,  Ridge, LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, classification_report
from sklearn.externals import joblib
import pandas as pd
import numpy as np
# 获取数据
lb = load_boston()

# 分割数据集到训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size=0.25)

# 特征值和目标值是都必须进行标准化处理, 实例化两个标准化API
std_x = StandardScaler()

x_train = std_x.fit_transform(x_train)
x_test = std_x.transform(x_test)

# 目标值
std_y = StandardScaler()

y_train = std_y.fit_transform(y_train)
y_test = std_y.transform(y_test)

# 正规方程求解方式预测结果
lr = LinearRegression()
lr.fit(x_train, y_train)
print(lr.coef_)
保存训练好的模型
joblib.dump(lr, "test.pkl")
# 预测测试集的房子价格
y_lr_predict = std_y.inverse_transform(lr.predict(x_test))
print("正规方程测试集里面每个房子的预测价格：", y_lr_predict)
print("正规方程的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), y_lr_predict))

# 梯度下降进行房价预测
sgd = SGDRegressor()
sgd.fit(x_train, y_train)
print(sgd.coef_)
# 预测测试集的房子价格
y_sgd_predict = std_y.inverse_transform(sgd.predict(x_test))
print("梯度下降测试集里面每个房子的预测价格：", y_sgd_predict)
print("梯度下降的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), y_sgd_predict))

# 岭回归进行房价预测
rd = Ridge(alpha=1.0)
rd.fit(x_train, y_train)
print(rd.coef_)
# 预测测试集的房子价格
y_rd_predict = std_y.inverse_transform(rd.predict(x_test))
print("梯度下降测试集里面每个房子的预测价格：", y_rd_predict)
print("梯度下降的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), y_rd_predict))

sklearn.metrics.mean_squared_error
mean_squared_error(y_true, y_pred)
均方误差回归损失
y_true:真实值
y_pred:预测值
return:浮点数结果