bips-hb/cpi: Conditional Predictive Impact
A general test for conditional independence in supervised learning algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>. Implements a conditional variable importance measure which can be applied to any supervised learning algorithm and loss function. Provides statistical inference procedures without parametric assumptions and applies equally well to continuous and categorical predictors and outcomes.
Getting started
Browse package contents
Package details |
|
|---|---|
| Maintainer | Marvin N. Wright <cran@wrig.de> |
| License | GPL (>= 3) |
| Version | 0.1.5 |
| URL | https://github.com/bips-hb/cpi https://bips-hb.github.io/cpi/ |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
|
