Using Smote with Gridsearchcv in Scikit-learn

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

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.