# K-Fold Cross-Validation With Only a Low Identification Accuracy for the First Fold

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?

Thanks.

• How many "examples" are there that the K-Fold operates upon? – A_A Mar 14 at 13:17
• Thanks for your response. Fine-tuned on a small dataset with 2420 examples. – I.O Animasahun Mar 15 at 3:49
• And this discrepancy is consistent across more than one runs? – A_A Mar 15 at 7:30
• not consistent. Only with the first fold (70%) while others gave me 100%. Why the discrepancy is only with the first fold and other folds gave good results? Is this strange and if not, what could be the likely explanation for this? – I.O Animasahun Mar 15 at 9:39
• If you run K-fold once, it would run the computation partitioning the data in one way. If you run it again, the data will be partitioned in a different way. Is this behaviour that you report consistent between runs? – A_A Mar 15 at 10:55