Stability Assessment of Statistical Learning Methods

Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning.

Stability Assessment of Statistical Learning Methods in R

Overview

The R package stablelearner provides:

• Stability assessment for tree learners: stabletree and accompanying methods, including coercion functions for various random forest objects to stabletree objects.

• Stability assessment for supervised statistical learners in general: stability and accompanying methods, including a broad range of similarity measures for both classification and regression problems.

References

Philipp M, Zeileis A, Strobl C (2016). "A Toolkit for Stability Assessment of Tree-Based Learners." In Colubi A, Blanco A, Gatu C (eds.), Proceedings of COMPSTAT 2016 - 22nd International Conference on Computational Statistics, 315-325. ISBN 978-90-73592-36-0. Preprint available at https://EconPapers.RePEc.org/RePEc:inn:wpaper:2016-11

Philipp M, Rusch T, Hornik K, Strobl C (2018). "Measuring the Stability of Results from Supervised Statistical Learning." Journal of Computational and Graphical Statistics, 27(4), 685-700. doi:10.1080/10618600.2018.1473779

Installation

The stable version of stablelearner is available from CRAN:

install.packages("stablelearner")

The latest development version can be installed from R-universe:

install.packages("stablelearner", repos = "https://zeileis.R-universe.dev")

License

The package is available under the General Public License version 3 or version 2