What is the best validation method?

What is the best validation method for my machine learning model?

The best validation method often depends on your dataset and model type. Common methods include k-fold cross-validation and train-test split.

Can you explain k-fold cross-validation?

Sure! K-fold cross-validation involves splitting your dataset into 'k' subsets. The model is trained on 'k-1' subsets and validated on the remaining subset. This process is repeated 'k' times.

What are the advantages of using k-fold cross-validation?

K-fold cross-validation provides a more reliable estimate of model performance, reduces overfitting, and makes better use of limited data.

Are there any situations where I shouldn't use k-fold cross-validation?

Yes, if your dataset is very small, k-fold might not be ideal. In such cases, a simple train-test split could be more effective.