mlsanityData to 14 Oct 2026

pystacked: Stacking generalization and machine learning in Stata

arXiv 2208.10896 ↗First posted 23 Aug 2022econ.EM stat.MLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 40 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

3 of 40 references.

References · 3

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019Machine Learning Methods Economists Should Know Aboutcited by 64
  3. 2021Machine Learning using Stata/Pythoncited by 2

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Citing papers per year: 2023: 1.

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