\textsc{CaDET}: interpretable parametric conditional density estimation with decision trees and forests

DOI10.1007/s10994-019-05820-3zbMath1494.68211OpenAlexW2954754443WikidataQ127616635 ScholiaQ127616635MaRDI QIDQ2320591

Cyrus Cousins, Matteo Riondato

Publication date: 23 August 2019

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-019-05820-3

zbMATH Keywords

sufficient statisticsparametric modelsrandom forests

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• Scikit
• RFCDE

Cites Work

• A Mathematical Theory of Communication
• Hellinger distance and Kullback-Leibler loss for the kernel density estimator
• Nonparametric estimation of conditional quantiles using quantile regression trees
• Admissible probability measurement procedures
• Applied Linear Regression
• Application of the Radon-Nikodym Theorem to the Theory of Sufficient Statistics
• Random forests
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