Machine Learning with ML.NET - Random Forest

One of the most popular ways to build ensembles is to use the same algorithm multiple times but on the different subsets of the training dataset. Techniques that are used for this are called bagging and pasting. The only difference in these techniques is that while building subsets bagging allows training instances to be sampled several times for the same predictor, while pasting is not allowing that. When all algorithms are trained, the ensemble makes a prediction by aggregating the predictions of all algorithms. In the classification case that is usually the hard-voting process, while for the regression average result is taken.