In all the ensembles we have learned about so far, we have manipulated the dataset in certain ways and exposed subsets of the data for model building. However, in stacking, we are not going to do anything with the dataset; instead we are going to apply a different technique that involves using multiple ML algorithms instead. In stacking, we build multiple models with various ML algorithms. Each algorithm possesses a unique way of learning the characteristics of data and the final stacked model indirectly incorporates all those unique ways of learning. Stacking gets the combined power of several ML algorithms through getting the final prediction by means of voting or averaging as we do in other types of ensembles.