Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

Assemble an efficient interpretable machine learning workflow.

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

In this repository you will fine explainability of machine learning models.

📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins

Code for EACL Workshop paper Can BERT eat RuCoLA? Topological Data Analysis to Explain

Using a Kaggle dataset, customer personality was analysed on the basis of their spending habits, income, education, and family size. K-Means, XGBoost, and SHAP Analysis were performed.

Code for my thesis about SHAP. Implementation of DecisionTree, SVM, BERT on 2 Datasets Imdb and Argument Mining

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.

Measuring galaxy environmental distance scales with GNNs and explainable ML models

Getting explanations for predictions made by black box models.

Determining Feature Importance by Integrating Random Forest and SHAP in Python

Implementation of the algorithm described in the paper "An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data"

gradient-boosted regression and decision tree models on behavioural animal data (PLOS Computational Biology, doi: https://doi.org/10.1371/journal.pcbi.1011985)

Android malware detection using machine learning.

XGB - SHAP XAI

Holistic Multimodel Domain Analysis: A New Paradigm for Robust, Transparent, And Reliable Exploratory Machine Learning that Considers Cross-Model Variability in Feature Importance Assessment

No-code Machine learning (Pre-alpha)