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19 search results found

Interpret ⭐ 5,992

Fit interpretable models. Explain blackbox machine learning.

Dalex ⭐ 1,242

moDel Agnostic Language for Exploration and eXplanation

Interpretable_machine_learning_with_python ⭐ 629

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

Mli Resources ⭐ 405

H2O.ai Machine Learning Interpretability Resources

Modelstudio ⭐ 305

📍 Interactive Studio for Explanatory Model Analysis

Adversarial Explainable Ai ⭐ 235

💡 Adversarial attacks on explanations and how to defend them

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Treeshap ⭐ 72

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Shapml.jl ⭐ 50

A Julia package for interpretable machine learning with stochastic Shapley values

Xai Iml Sota ⭐ 50

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

Diabetes_use_case ⭐ 19

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

A Python package with explanation methods for extraction of feature interactions from predictive models

Interpretable Ml ⭐ 17

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Variable importance via oscillations

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

Article Information 2019 ⭐ 10

Article for Special Edition of Information: Machine Learning with Python

Jsm_2018_paper ⭐ 8

Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlinepro

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

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