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the part I always find underrated is that feature selection changes depending on the model class. wrapper methods can overfit to the estimator you tune with, while embedded ones bake it in. do you cover how to keep the selection from leaking into eval?

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Just when I needed it 🫡

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Ever run a long GridSearchCV and had no idea if it was doing anything? sklearn 1.9 adds a callback API. Attach a ProgressBar or ScoringMonitor — and watch it work, iteration by iteration. @scikit-learn.org

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Kapalı içerik

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