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Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

python data-science machine-learning data-mining h2o gradient-boosting-machine transparency decision-tree fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability

Updated Feb 10, 2021
Jupyter Notebook

h2oai / mli-resources

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H2O.ai Machine Learning Interpretability Resources

python data-science machine-learning data-mining h2o xgboost transparency jupyter-notebooks fairness accountability interpretability interpretable-ai interpretable-ml explainable-ml mli xai fatml interpretable-machine-learning iml machine-learning-interpretability

Updated Dec 12, 2020
Jupyter Notebook

ModelOriented / modelStudio

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📍 Interactive Studio for Explanatory Model Analysis

machine-learning predictive-modeling interactive-visualizations interpretability explainable-artificial-intelligence explainable-ai explainable-ml xai model-visualization interpretable-machine-learning iml explainability explanatory-model-analysis explainable-machine-learning

Updated Mar 6, 2022
R

hbaniecki / adversarial-explainable-ai

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💡 Adversarial attacks on model explanations, and evaluation approaches

security evaluations attacks interpretability robustness adversarial-machine-learning adversarial-examples adversarial-attacks model-explanation interpretable-deep-learning interpretable-ai explainable-ai explainable-ml xai interpretable-machine-learning iml explainability responsible-ai adversarial-defense adversarial-xai

Updated Mar 14, 2022

pbiecek / breakDown

Sponsor

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Model Agnostics breakDown plots

data-science machine-learning interpretability xai visual-explanations iml

Updated Apr 4, 2020
R

ModelOriented / iBreakDown

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

breakdown interpretability xai shapley iml

Updated Feb 6, 2022
R

nredell / ShapML.jl

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A Julia package for interpretable machine learning with stochastic Shapley values

julia feature-importance shapley interpretable-machine-learning iml shap shapley-value stochastic-shapley-values

Updated Feb 21, 2022
Julia

nredell / shapFlex

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An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

package machine-learning r ensemble r-package causality causal-inference feature-importance causal-networks shapley interpretable-machine-learning iml shap shapley-value shapley-values

Updated Jun 9, 2020
R

rehmanzafar / xai-iml-sota

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Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

machine-learning human-in-the-loop interpretability explainable-artificial-intelligence researchers interactive-machine-learning deep-learning-visualization human-in-the-loop-machine-learning explainable-ml xai interpretable interpretable-machine-learning iml model-interpretability explainable interpretable-models explainable-models interpretable-learning explaining-ai explanation-methods

Updated Dec 15, 2021
R

12wang3 / rrl

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The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".

scalability representation-learning nips interpretability interpretable-ai interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning iml explainability neurips interpretable-classification rule-based-model neuro-symbolic-ai

Updated Feb 9, 2022
Python

ModelOriented / live

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Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

machine-learning lime interpretability xai model-visualization visual-explanations iml