scripts used for neural decoding of single and multi unit auditory cortex data

Real-time explainable machine learning for business optimisation

A game theoretic approach to explain the output of any machine learning model.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

This repository contains the Python scripts that I have written and run to execute a series of analytic model developments using datasets taken from the book "The Elements of Statistical Elements" by Hastie, Tibshirani, Friedman

Comparisons of methods used to measure model interactions

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

Binary Classification with Bank Churn Dataset

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.

A power-full Shapley feature selection method.

Scripts for generating SHAP & LIME explainations and their corresponding plots

gradient-boosted regression and decision tree models on behavioural animal data

Automated Tool for Optimized Modelling

Montimage AI Platform (MAIP) provides users with easy access to AI services developed by Montimage, through a friendly and intuitive interface.

A machine learning implementation of an interpretable model for membrane separation performance prediction of COFs materials.

Multi-classification of ocular diseases using ODIR-5K and uses explainable AI methods to improve the transparency of computer aided diagnostic systems.

This repo is all about feature importance. Whereby we look at the ways one can identify if a feature is worth having in the model or rather if it has a significant influence in the prediction. The methods are model-agnostic.

Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.

deciphering car VIN codes using regular expressions