Fit interpretable models. Explain blackbox machine learning.
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Updated
Mar 20, 2023 - C++
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome machine learning interpretability resources.
moDel Agnostic Language for Exploration and eXplanation
Generate Diverse Counterfactual Explanations for any machine learning model.
[HELP REQUESTED] Generalized Additive Models in Python
Interesting resources related to XAI (Explainable Artificial Intelligence)
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
PiML (Python Interpretable Machine Learning) toolbox for model development and model validation
OmniXAI: A Library for eXplainable AI
H2O.ai Machine Learning Interpretability Resources
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI
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PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
Concept Bottleneck Models, ICML 2020
All about explainable AI, algorithmic fairness and more
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