What is Stacking?

Stacking combines several predictors (here, several blends) with weights fitted on their predictions for data not used to build them: Breiman’s (1996) stacked regressions use cross-validated predictions and least squares under non-negativity constraints, an idea he credits to Wolpert (1992).