Stacked generalization is another method of combining estimators to reduce their biases [W1992] by combining several estimators (possibly non-linearly) stacked together in layers. Each layer will contain estimators and their predictions are used as features to the next layer.
As stacked generalization is a generic framework for combining supervised estimators, it works with regression and classification problems. The API reflects that, so it's the same for both categories.
The intent of this user guide is to serve both as an introduction to stacked generalization and as a manual for using the framework.
user_guide/intro user_guide/strategies user_guide/usage
References