What is the impact of the K value in K-Fold cross-validation?

What is the impact of the K value in K-Fold cross-validation?

The K value in K-Fold cross-validation determines how many subsets the data is divided into. A higher K means more folds, which can lead to a better estimate of model performance but also increases computation time.

How do I choose the right K value for my model?

Choosing the right K value often depends on the size of your dataset. A common practice is to use K=5 or K=10, but you can experiment with different values to see which gives the best performance.

What are the trade-offs of using a higher K value?

Using a higher K value can provide a more accurate estimate of model performance, but it also increases the risk of overfitting and requires more computational resources.

Can K-Fold cross-validation be used for all types of models?

Yes, K-Fold cross-validation can be applied to most machine learning models, but it's particularly useful for models where overfitting is a concern.

What happens if K is set too low?

If K is set too low, the model evaluation may be biased, as the training and validation sets may not be representative of the overall dataset.