I am using K-Fold cross validation from
sklearn.model_selection for evaluating the performance of my model. K=10 and the K-fold cross-validation is set as:
kfcv=Kfold(n_splits=10, random_state=0, shuffle=True)
The result of the first fold is 70% while remaining 9 folds are 100%. I have set random state to another value (such as 50), the same problem.
Why is the high discrepancy only with the first fold? I have used 5 fold and the same problem with the first fold. I expect that other folds should also reflects a decrease since the division is random and I also set shuffle to be true.
Is there anything am doing wrongly? If not, what would be the likely explanation for this?