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.
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.
Heart disease prediction by exploring different models, and feature importance visualization
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
Android malware detection using machine learning.
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
Shapley values for JSM-method in terms of Concept Lattices (FCA)
This is a visual and interactive part of a bigger Adults project. Income prediction is based on Random Forest model. Front part is created with dash framework
Reference implementation of the paper Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data
A Julia port of the fastshap package in R
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
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