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Im trying to use gridsearch to find the best parameter for my model. Knowing that I have to implement nearmiss undersampling method while doing cross validation, should I fit my gridsearch on my undersampled dataset (no matter which under sampling techniques) or on my entire training data (whole dataset) before using cross validation?

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Do grid search on the same Level of "imbalancedeness" that you plan/are able to do your Training and Evaluation on.

So that means that if you saw that imbalanced data set does not skew your model predictions or results in other unwanted Outcomes, done use the maximal dataset possible. But on the other Hand if your model is strongly overfitting because of imbalanced dataset then optimisation with grid search will make him overfit more in that direction.

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  • $\begingroup$ Thank you very much for your answer ! So if my dataset is imbalance, there is no point using grid search on a resampled dataset right ? $\endgroup$ – Valentin Feb 16 at 15:28

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