pystacked: Stacking generalization and machine learning in Stata

Autor:	Ahrens, Achim, Hansen, Christian B., Schaffer, Mark E.
Rok vydání:	2022
Předmět:	Economics - Econometrics Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 (multi-layer perceptron). pystacked can also be used with as a `regular' machine learning program to fit a single base learner and, thus, provides an easy-to-use API for scikit-learn's machine learning algorithms. Comment: The pystacked package is available here: https://github.com/aahrens1/pystacked
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.10896 Zobrazit plný text záznamu View this record from Arxiv