【机器学习实战】使用sklearn中的LinearRegression或SGDRegressor实现波士顿房价的回归预测

1. 数据集 —— 波士顿房价

给定的这些特征，是专家们得出的影响房价的结果属性。我们此阶段不需要自己去探究特征是否有用，只需要使用这些特征。到后面量化很多特征需要我们自己去寻找

2. 实现

2.1 代码

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

def linear():
    '''
    正规方程的优化方法对波士顿房价进行预测
    :return:
    '''
    # 1. 获取数据集
    boston = load_boston()

    # 2. 划分数据集
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=22)

    # 3. 特征工程——标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)

    # 4. 线性回归预估器
    estimator = LinearRegression()
    estimator.fit(x_train, y_train)

    # 5. 得到模型
    print('权重系数：\n', estimator.coef_) # 系数w
    print('偏置为：\n', estimator.intercept_) # 偏移量b

    # 6. 模型优化(评价指标，均方差MSE）
    y_predict = estimator.predict(x_test)
    print('预测房价：\n', y_predict)
    mse = mean_squared_error(y_test, y_predict)
    print('正规方程-均方误差为：\n', mse)

    return None

def SGD_linear():
    '''
    梯度下降的优化方式对波士顿房价进行预测
    :return:
    '''

    boston = load_boston()
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=22)
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)
    estimator = SGDRegressor(eta0=0.005, max_iter=10000)
    estimator.fit(x_train, y_train)
    print('权重系数：\n', estimator.coef_) # 系数w
    print('偏置为：\n', estimator.intercept_) # 偏移量b
    y_predict = estimator.predict(x_test)
    print('预测房价：\n', y_predict)
    mse = mean_squared_error(y_test, y_predict)
    print('梯度下降-均方误差为：\n', mse)


if __name__ == '__main__':
    linear()
    SGD_linear()

2.2 结果