What are the disadvantages of using a holdout method in machine learning?

What are the disadvantages of using a holdout method in machine learning?

The holdout method can lead to several disadvantages, such as overfitting, underfitting, and not utilizing all available data.

Can you explain how overfitting occurs with the holdout method?

Overfitting happens when the model learns the noise in the training data instead of the underlying pattern, leading to poor generalization.

What are some alternatives to the holdout method?

Alternatives include k-fold cross-validation, stratified sampling, and bootstrapping, which can provide more reliable estimates.

How does the holdout method affect model evaluation?

It can lead to biased evaluation metrics if the holdout set is not representative of the overall data distribution.

Is there a situation where the holdout method is still useful?

Yes, it can be useful for quick assessments or when computational resources are limited, but caution is advised.