机器学习：线性回归和岭回归入门代码

机器学习中运用python进行对房子价格的预测代码，数据库直接使用sklearn自带的boston，使用三种方法进行预测，分别是：线性回归直接预测、梯度下降预测、岭回归预测

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression, SGDRegressor,Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error


def mylinear():
    """
    线性回归直接预测房子价格
    :return: None
    """

    # 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)

    # print(y_train, y_test)

    # 进行标准化处理(?)目标值处理？
    # 特征值和目标值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.reshape(-1, 1))
    y_test = std_y.transform(y_test.reshape(-1, 1))

    # estimator预测
    # 正规方程求解方式预测结果
    lr = LinearRegression()

    lr.fit(x_train, y_train)

    print(lr.coef_)



    # 预测测试集房子价格
    y_lr_predict = std_y.inverse_transform(lr.predict(x_test))

    print("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))
    # 梯度下降去预测房价
    sgd = SGDRegressor()

    sgd.fit(x_train, y_train)

    print(sgd.coef_)



    # 预测测试集房子价格
    y_sgd_predict = std_y.inverse_transform(sgd.predict(x_test))

    print("测试集里面每个房子的预测价格：", y_sgd_predict)

    print("梯度下降方程的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), y_sgd_predict))

    # 岭回归去预测房价
    rd = Ridge()

    rd.fit(x_train, y_train)

    print(rd.coef_)



    # 预测测试集房子价格
    y_rd_predict = std_y.inverse_transform(rd.predict(x_test))

    print("测试集里面每个房子的预测价格：", y_rd_predict)

    print("岭回归方程的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), y_rd_predict))



    return None




if __name__ == '__main__':
    mylinear()