Why do employees leave? This project first compares the predictive performance of three different models, then uses the best model to help reveal the top contributing factors.
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Updated
May 24, 2022 - Jupyter Notebook
Why do employees leave? This project first compares the predictive performance of three different models, then uses the best model to help reveal the top contributing factors.
Heart disease prediction by exploring different models, and feature importance visualization
Python/Jupyter Notebook to my Bachelor-Thesis in Computer Science. Explains contributions of features that are not part of a Machine Learning model by using Transfer Learning and Shapley Values/SHAP.
Group Recommendation Systems with Diversity-based Clustering and Game Theory
Using SHAP values to explain model features
API backend to deploy a machine learning model to the web
ML implementations in Multi-scale model for lignin biosynthesis in Populus Trichocarpa
An investigation on the use of shapley explanations for unsupervised anomaly-detection models
Reference implementation of the paper Redundancy-aware unsupervised ranking based on game theory - application to gene enrichment analysis
Migration networks and housing prices analysis and ML tools
Source code for the Joint Shapley values: a measure of joint feature importance
API for ShapEmotionsCorrection project
HERALD: An Annotation Efficient Method to Train User Engagement Predictors in Dialogs (ACL 2021)
A method for conditional shapley value estimation, built off the shapr package: https://github.com/NorskRegnesentral/shapr/tree/master
Analysis of baseball stats using ML w/ feature explainability
In this paper we researched the accuracy and usability of machine learning models for MMM analyses.
Final Year Project KCL
[IJCAI 2024] Redefining Contributions: Shapley-Driven Federated Learning
Android malware detection using machine learning.
A Julia port of the fastshap package in R
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