iBreakDown

Model explainer

A tool for explaining predictions from machine learning models by attributing them to specific input variables and their interactions.

Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)

GitHub

82 stars
10 watching
15 forks
Language: R
last commit: about 1 year ago
breakdownimlinterpretabilityshapleyxai

Related projects:

Repository Description Stars
modeloriented/modelstudio A tool for creating interactive, model-agnostic explanations of machine learning models in R 328
modeloriented/dalex A tool to help understand and explain the behavior of complex machine learning models 1,390
mi2datalab/pybreakdown A Python implementation of a method to explain the predictions of machine learning models 42
rmarko/explainprediction An R package for explaining the predictions made by machine learning models in data science applications. 2
giuseppec/iml Provides methods to interpret and explain the behavior of machine learning models 494
modeloriented/randomforestexplainer A set of tools to provide insights into the workings of an ensemble machine learning model. 230
explainx/explainx A framework to explain and debug blackbox machine learning models with a single line of code. 419
modeloriented/fairmodels A tool for detecting bias in machine learning models and mitigating it using various techniques. 86
marcotcr/anchor Provides a method to generate explanations for predictions made by any black box classifier. 798
modeloriented/drwhy A collection of tools and guidelines for building responsible machine learning models 682
thomasp85/lime An R package for providing explanations of predictions made by black box classifiers. 486
interpretml/dice Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. 1,373
marcelrobeer/explabox An exploratory tool for analyzing and understanding machine learning models 14
modeloriented/ingredients Provides tools to assess and visualize the importance and effects of features in machine learning models 37
pbiecek/xaiaterum2020 An R package and workshop materials for explaining machine learning models using explainable AI techniques 52