# small dataset CV

I have a very small dataset ( 150 records) with 20 features, trying to predict a binary outcome. Due to the small size, i chose to do 10 CV instead of train/test as the train/test split.

I was wondering if i'm doing a GridsearchCV on 10-fold, getting the best parameters, and then using those parameters evaluating the performance on 10-fold - is that "legal" or overfitting? am i suppose to run the best parameters on the entire data ? or can i use 10-fold again?

also, will LOOCV suppose to give better generalization on the performance part? (not on the gridsearch)?

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Leave one out CV is exactly the same as $$k$$-fold CV but with $$k$$ equal to the number of instances, so in your case it's like 150-fold CV. Advantage: more training data every time, so potentially better model; disadvantage: computational cost higher since the training/testing is repeated 150 times.