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

1. What is k-fold cross-validation accuracy?
2. Does k-fold cross-validation increase accuracy?
3. What is a weakness of k-fold cross-validation?
4. What is the best k-fold cross-validation?

What is k-fold cross-validation accuracy?

It has a mean validation accuracy of 93.85% and a mean validation f1 score of 91.69%.

Does k-fold cross-validation increase accuracy?

The reason why the accuracy score has been increased by 6% after applying k-fold cross-validation is that the cross-validation procedure has averaged out 10 sets of accuracy scores by splitting the dataset into 10 different folds (specified as cv=10).

What is a weakness of k-fold cross-validation?

Higher Training Time: with cross-validation, we need to train the model on multiple training sets. Expensive Computation: Cross-validation is computationally very expensive as we need to train on multiple training sets.

What is the best k-fold cross-validation?

In most cases, the choice of k is usually 5 or 10, but there is no formal rule. However, the value of k relies upon the size of the dataset. The runtime of the cross-validation algorithm and the computational cost with large values of k.