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)
81 stars
10 watching
15 forks
Language: R
last commit: 12 months ago breakdownimlinterpretabilityshapleyxai
Related projects:
Repository | Description | Stars |
---|---|---|
modeloriented/modelstudio | A tool for creating interactive, model-agnostic explanations of machine learning models in R | 326 |
modeloriented/dalex | A tool to help understand and explain the behavior of complex machine learning models | 1,375 |
mi2datalab/pybreakdown | A Python implementation of a method to explain the predictions of machine learning models | 41 |
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 | 492 |
modeloriented/randomforestexplainer | A set of tools to provide insights into the workings of an ensemble machine learning model. | 230 |
explainx/explainx | Provides a framework to understand and explain the behavior of machine learning models used in data science applications. | 417 |
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 | 680 |
thomasp85/lime | An R package for providing explanations of predictions made by black box classifiers. | 485 |
interpretml/dice | Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. | 1,364 |
marcelrobeer/explabox | An exploratory tool for analyzing and understanding machine learning models | 15 |
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 |