Litcius/Paper detail

pystacked: Stacking generalization and machine learning in Stata

Achim Ahrens, Christian Hansen, Mark E. Schaffer

2023The Stata Journal Promoting communications on statistics and Stata28 citationsDOIOpen Access PDF

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.

Topics & Concepts

Python (programming language)Computer scienceArtificial intelligenceMachine learningGeneralizationArtificial neural networkSupport vector machineStackingPerceptronRandom forestMultilayer perceptronBase (topology)Binary classificationBinary numberMathematicsProgramming languageArithmeticMathematical analysisPhysicsNuclear magnetic resonanceStatistical Methods and InferenceProbability and Statistical ResearchStatistical and numerical algorithms