Abstract
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 program to fit one base learner and thus provides an easy-to-use application programming interface for scikit-learn‘s machine learning algorithms.
Original language | English |
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Pages (from-to) | 909-931 |
Number of pages | 23 |
Journal | The Stata Journal |
Volume | 23 |
Issue number | 4 |
Early online date | 21 Dec 2023 |
DOIs | |
Publication status | Published - Dec 2023 |
Keywords
- Python
- machine learning
- model averaging
- pystacked
- sci-kit learn
- st0731
- stacked generalization
ASJC Scopus subject areas
- Mathematics (miscellaneous)