TY - UNPB
T1 - pystacked: Stacking generalization and machine learning in Stata
AU - Ahrens, Achim
AU - Hansen, Christian B.
AU - Schaffer, Mark Edwin
PY - 2022/8/23
Y1 - 2022/8/23
N2 - pystacked implements stacked generalization (Wolpert, 1992) for regression and binary classification via Python's scikit-lear}. 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.
AB - pystacked implements stacked generalization (Wolpert, 1992) for regression and binary classification via Python's scikit-lear}. 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.
KW - machine learning
KW - stacked generalization
KW - model averaging
KW - Stata
KW - Python
KW - sci-kit learn
UR - https://github.com/aahrens1/pystacked
UR - http://ideas.repec.org/c/boc/bocode/s459115.html
U2 - 10.48550/arXiv.2208.10896
DO - 10.48550/arXiv.2208.10896
M3 - Working paper
BT - pystacked: Stacking generalization and machine learning in Stata
ER -