The confirmed approach to choosing the number of principal components for prediction models includes exploring the contribution of each principal component to the total variance of the target variable. A combination of possible important principal components can be chosen to explain a big part...

Background: The bootstrap can be alternative to cross-validation as a training/test set splitting method since it minimizes the computing time in classification problems in comparison to the tenfold cross-validation. Objectives: Тhis research investigates what proportion should be used to split...

In this research, we propose the bootstrap procedure as a method for train/test splitting in machine learning algorithms for classification. We show that this resampling method can be a reliable alternative to cross validation and repeated random test/train splitting algorithms. The bootstrap...

In this research, we propose the bootstrap procedure as a method for train/test splitting in machine learning algorithms for classification. We show that this resampling method can be a reliable alternative to cross validation and repeated random test/train splitting algorithms. The bootstrap...