There are 2 repositories under shapley-additive-explanations topic.
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins
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.
In this repository you will fine explainability of machine learning models.
Code for EACL Workshop paper Can BERT eat RuCoLA? Topological Data Analysis to Explain
Measuring galaxy environmental distance scales with GNNs and explainable ML models
gradient-boosted regression and decision tree models on behavioural animal data (PLOS Computational Biology, doi: https://doi.org/10.1371/journal.pcbi.1011985)
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.
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
Implementation of the algorithm described in the paper "An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data"
Android malware detection using machine learning.
XGB - SHAP XAI
In this project we predict credit card defaults using classification models.
The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition.
Code for my thesis about SHAP. Implementation of DecisionTree, SVM, BERT on 2 Datasets Imdb and Argument Mining
Determining Feature Importance by Integrating Random Forest and SHAP in Python
XAI analytics to understand the working of SHAP values
XAI analytics to understand the working of SHAP values
Getting explanations for predictions made by black box models.
An Analysis of Lassa Fever Outbreaks in Nigeria using Machine Learning Models and Shapley Values
ML implementations in Multi-scale model for lignin biosynthesis in Populus Trichocarpa
Coding challenge for a job interview examining the predictors of vehicle accident severity using GB Road Safety Data
Frontend for ShapEmotionsCorrectionAPI
Understanding the limitations of Gassmann's fluid substitution model using explainable ML
credit default prediction app
Use machine learning to find out what drives sales and predict sales
👨💻 This repository shows how machine learning and SHAP can be leveraged to understand the reasons of production downtime ⌛
AI applications can be found in various real-world systems, including vehicle system design and real-time car accident prediction. There is an increasing need to better explain AI-driven processes, especially in terms of potential legal disputes that might result from AI decisions. This analysis addresses this explainability and legal issues.
This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.
Explores diabetes prediction using various ML models and XAI techniques (SHAP, LIME, ALE) on the Pima Indian Diabetes dataset.
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.
Predict probability of default on credit