在 Scikit-learn 中使用 Smote 和 Gridsearchcv

Question

I'm dealing with an imbalanced dataset and want to do a grid search to tune my model's parameters using scikit's gridsearchcv. To oversample the data, I want to use SMOTE, and I know I can include that as a stage of a pipeline and pass it to gridsearchcv. My concern is that I think smote will be applied to both train and validation folds, which is not what you are supposed to do. The validation set should not be oversampled. Am I right that the whole pipeline will be applied to both dataset splits? And if yes, how can I turn around this? Thanks a lot in advance

Answer 1

Yes, it can be done, but with imblearn Pipeline .

You see, imblearn has its own Pipeline to handle the samplers correctly. I described this in a similar question here .

When called predict() on a imblearn.Pipeline object, it will skip the sampling method and leave the data as it is to be passed to next transformer. You can confirm that by looking at the source code here :

        if hasattr(transform, "fit_sample"):
            pass
        else:
            Xt = transform.transform(Xt)

So for this to work correctly, you need the following:

from imblearn.pipeline import Pipeline
model = Pipeline([
        ('sampling', SMOTE()),
        ('classification', LogisticRegression())
    ])

grid = GridSearchCV(model, params, ...)
grid.fit(X, y)

Fill the details as necessary, and the pipeline will take care of the rest.