Having tested our previous approach, it is interesting to test the same process on the skewed data. Our intuition is that skewness will introduce issues that are difficult to capture and therefore provide a less effective algorithm. To be fair, taking into account the fact that the train and test datasets are substantially bigger than the under-sampled ones, it is necessary to have a K-fold cross-validation. We can split the data: 60% for the training set, 20% for cross validation, and 20% for the test data. But let's take the same approach as before (there's no harm in this; it's just that K-fold is computationally more expensive):
best_c = print_gridsearch_scores(X_train,y_train)
Best parameters ...
