iml: An R package for Interpretable Machine 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 Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Journal/Conference Name J. Open Source Software
Paper Category
Paper Abstract Complex, non-parametric models, which are typically used in machine learning, have proven to be successful in many prediction tasks. But these models usually operate as black boxes: While they are good at predicting, they are often not interpretable. Many inherently interpretable models have been suggested, which come at the cost of losing predictive power. Another option is to apply interpretability methods to a black box model after model training. Given the velocity of research on new machine learning models, it is preferable to have model-agnostic tools which can be applied to a random forest as well as to a neural network. Tools for model-agnostic interpretability methods should improve the adoption of machine learning.
Date of publication 2018
Code Programming Language R

Copyright Researcher 2022