Showing 3 of total 3 results (show query)
modeloriented
treeshap:Compute SHAP Values for Your Tree-Based Models Using the 'TreeSHAP' Algorithm
An efficient implementation of the 'TreeSHAP' algorithm introduced by Lundberg et al., (2020) <doi:10.1038/s42256-019-0138-9>. It is capable of calculating SHAP (SHapley Additive exPlanations) values for tree-based models in polynomial time. Currently supported models include 'gbm', 'randomForest', 'ranger', 'xgboost', 'lightgbm'.
Maintained by Mateusz Krzyzinski. Last updated 1 years ago.
explainabilityexplainable-aiexplainable-artificial-intelligenceexplanatory-model-analysisimlinterpretabilityinterpretable-machine-learningmachine-learningresponsible-mlshapshapley-valuexaicpp
83 stars 6.69 score 170 scriptsmodeloriented
arenar:Arena for the Exploration and Comparison of any ML Models
Generates data for challenging machine learning models in 'Arena' <https://arena.drwhy.ai> - an interactive web application. You can start the server with XAI (Explainable Artificial Intelligence) plots to be generated on-demand or precalculate and auto-upload data file beside shareable 'Arena' URL.
Maintained by Piotr Piątyszek. Last updated 5 years ago.
axplainable-artificial-intelligenceemaexplainabilityexplanatory-model-analysisimlinteractive-xaiinterpretabilityxai
31 stars 5.94 score 14 scripts
persimune
explainer:Machine Learning Model Explainer
It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements.
Maintained by Ramtin Zargari Marandi. Last updated 6 months ago.
aiclassificationclinical-researchexplainabilityexplainable-aiinterpretabilitymachine-learningregressionshapstatistics
15 stars 5.43 score 12 scripts