sklearn GridSearchCV 与管道 [英] sklearn GridSearchCV with Pipeline

问题描述

我是 sklearn 的 Pipeline 和 GridSearchCV 功能的新手.我正在尝试构建一个管道，该管道首先对我的训练数据执行 RandomizedPCA，然后拟合岭回归模型.这是我的代码:

I'm new to sklearn's Pipeline and GridSearchCV features. I am trying to build a pipeline which first does RandomizedPCA on my training data and then fits a ridge regression model. Here is my code:

pca = RandomizedPCA(1000, whiten=True)
rgn = Ridge()

pca_ridge = Pipeline([('pca', pca),
                      ('ridge', rgn)])

parameters = {'ridge__alpha': 10 ** np.linspace(-5, -2, 3)}

grid_search = GridSearchCV(pca_ridge, parameters, cv=2, n_jobs=1, scoring='mean_squared_error')
grid_search.fit(train_x, train_y[:, 1:])

我知道 RidgeCV 函数，但我想尝试使用 Pipeline 和 GridSearch CV.

I know about the RidgeCV function but I want to try out Pipeline and GridSearch CV.

我希望网格搜索 CV 报告 RMSE 错误，但这在 sklearn 中似乎不受支持，所以我正在使用 MSE.然而，它重述的分数是负数:

I want the grid search CV to report RMSE error, but this doesn't seem supported in sklearn so I'm making do with MSE. However, the scores it resports are negative:

In [41]: grid_search.grid_scores_
Out[41]: 
[mean: -0.02665, std: 0.00007, params: {'ridge__alpha': 1.0000000000000001e-05},
 mean: -0.02658, std: 0.00009, params: {'ridge__alpha': 0.031622776601683791},
 mean: -0.02626, std: 0.00008, params: {'ridge__alpha': 100.0}]

显然这对于​​均方误差是不可能的 - 我在这里做错了什么?

Obviously this isn't possible for mean squared error - what am I doing wrong here?

推荐答案

那些分数是负 MSE 分数，即否定它们，你得到 MSE.事情是GridSearchCV，按照惯例，总是试图最大化它的分数，所以像MSE这样的损失函数必须被否定.

Those scores are negative MSE scores, i.e. negate them and you get the MSE. The thing is that GridSearchCV, by convention, always tries to maximize its score so loss functions like MSE have to be negated.

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