岭回归

忘是亡心i 2022-04-23 01:32 216阅读 0赞

岭回归即为带有L2正则化的线性回归，lambd（λ）为正则化力度=惩罚项系数。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RpYW53ZWkwODIy_size_16_color_FFFFFF_t_70][]

### 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 Ridge
    from sklearn.metrics import mean_squared_error
    from sklearn.externals import joblib
    
    def linear3():
        """
        岭回归对波士顿房价进行预测
        :return:
        """
        # 1）获取数据
        boston = load_boston()
        print("特征数量：\n", boston.data.shape)
    
        # 2）划分数据集
        x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=22)
    
        # 3）标准化
        transfer = StandardScaler()
        x_train = transfer.fit_transform(x_train)
        x_test = transfer.transform(x_test)
    
        # 4）预估器
        estimator = Ridge(alpha=0.5, max_iter=10000)
        estimator.fit(x_train, y_train)
    
        # 5）得出模型
        print("岭回归-权重系数为：\n", estimator.coef_)
        print("岭回归-偏置为：\n", estimator.intercept_)
    
        # 6）模型评估
        y_predict = estimator.predict(x_test)
        print("预测房价：\n", y_predict)
        error = mean_squared_error(y_test, y_predict)
        print("岭回归-均方误差为：\n", error)
    
        return None
    
    if __name__ == "__main__":
        linear3()

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