Holdout Validation
Holdout validation is a simple and widely used technique in machine learning for evaluating model performance by splitting a dataset into two distinct subsets: a training set and a test set. The model is trained on the training set and then evaluated on the test set to estimate its generalization ability on unseen data. This method helps prevent overfitting by providing an independent assessment of model accuracy.
Developers should use holdout validation when working with machine learning projects to quickly assess model performance, especially with large datasets where computational efficiency is important. It is particularly useful in initial model development phases, for comparing different algorithms, or in scenarios where data is abundant and a simple validation approach suffices, such as in many business applications or prototyping.