The pystacked command implements stacked generalization (Wolpert, 1992, Neural
Networks 5: 241–259) for regression and binary classification via Python's scikit-learn.
Stacking combines multiple supervised machine learners—the “base” or “level-0” learners—
into one learner. The currently supported base learners include regularized regression,
random forest, gradient boosted trees, support vector machines, and feed-forward neural
nets (multilayer perceptron). pystacked can also be used as a “regular” machine learning …

pystacked implements stacked generalization (Wolpert 1992) for regression and binary
classification via Python's scikit-learn. Stacking combines multiple supervised machine
learners—the “base” or “level-0” learners—into a single learner. The currently supported
base learners include regularized regression, random forest, gradient boosted trees, support
vector machines, and feed-forward neural nets (multilayer perceptron). pystacked can also
be used as a 'regular'machine learning program to fit a single base learner and, thus …