Measuring the Stability of Results from Supervised Statistical Learning

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Michel Philipp, Thomas Rusch, Kurt Hornik, Carolin Strobl
Journal/Conference Name Journal of Computational and Graphical Statistics
Paper Category
Paper Abstract ABSTRACTStability is a major requirement to draw reliable conclusions when interpreting results from supervised statistical learning. In this article, we present a general framework for assessing a...
Date of publication 2018
Code Programming Language R
Comment

Copyright Researcher 2021