README.md

In g-rho/eXplaAInability: Numerical and visual tools to analyse the explainability of partial dependence functions

eXplAInability

Numerical and visual tools to analyse the explainability of partial dependence functions.

Partial dependence plots are a popular tool to analyze black box machine learning models. The repo provides an R package that computes the explainability of a PDP, which is a measure to quantify how far a PDP is able to explain a model.

Supported functionalities: - ...computation of explainability, - ...matchplot of PDP vs. model predictions, - ...computation of a forward variable selection based on explainability, - ...visualization of 2D PDP vs. unexplained residual predictions, - ...scatterplot matrix of 2D partial dependence plots.

Details are described in this paper. The examples are taken from the paper.

g-rho/eXplaAInability documentation built on April 6, 2021, 1:42 a.m.