machine-learning-interpretability
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A curated list of awesome responsible machine learning resources.
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Nov 5, 2025
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
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Jun 17, 2024 - Jupyter Notebook
H2O.ai Machine Learning Interpretability Resources
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Dec 12, 2020 - Jupyter Notebook
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
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Aug 21, 2024 - Jupyter Notebook
Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign
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Dec 30, 2022 - R
Sample use case for Xavier AI in Healthcare conference:https://www.xavierhealth.org/ai-summit-day2/
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Jun 17, 2024 - Jupyter Notebook
An interpretable machine learning pipeline over knowledge graphs
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Apr 30, 2025 - Jupyter Notebook
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
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Mar 10, 2024 - Python
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
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Nov 19, 2019 - TeX
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
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Jun 21, 2022 - Jupyter Notebook
Article for Special Edition of Information: Machine Learning with Python
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Jan 8, 2025 - Jupyter Notebook
Paper for 2018 Joint Statistical Meetings:https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
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Dec 7, 2018 - TeX
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
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Aug 11, 2019 - JavaScript
INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.
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Aug 30, 2022 - Python
Rule Extraction from Bayesian Networks
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Jan 4, 2022 - Python
Overview of machine learning interpretation techniques and their implementations
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Jan 5, 2021 - Jupyter Notebook
Demonstration of InterpretME, an interpretable machine learning pipeline
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Feb 5, 2024 - Jupyter Notebook
This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".
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Jul 13, 2022 - Python
XMLX GitHub configuration
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Jul 26, 2024
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