Experiments on machine learning explainability
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Updated
Mar 26, 2021 - Jupyter Notebook
Experiments on machine learning explainability
Demo on performing Explainable AI using the SHAP Library
Exploration of ML Explainability Methods on the Statlog (Heart) Data Set
Awesome papers on Interpretable Machine Learning
This repository provides a summarization of recent empirical studies/human studies that measure human understanding with machine explanations in human-AI interactions.
Chest X-Ray Images (Pneumonia) classification: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
Validate the model card document in a GitHub action
Repository for the Linkit Beginner Challenge on Explainable ML using SHAP values.
Binary Classification with Neural Networks and Bayesian Optimization and SHAP Model Explanations
A method for conditional shapley value estimation, built off the shapr package: https://github.com/NorskRegnesentral/shapr/tree/master
Ths repo has the list of Interesting Literature in the domain of XAI
Research project on generation of counterfactuals for eXplainable AI, based on Bayesian Generation
Detecting sarcasm in Reddit comments
Paper and resources collections about interpretable AI (XAI)
Explain and interpret predictions of tree-based machine learning models
In this repository you will fine explainability of machine learning models.
Explaining blackbox predictions using python libraries.
A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.
Contribution to the PatchCamelyon Challenge (semester project in image processing)
Python functions to compute and plot global effects from ML models
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