interpretability-methods

ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

Pytorch implementation of various neural network interpretability methods

Learning clinical-decision rules with interpretable models.

Official code of the CVPR 2022 paper "Proto2Proto: Can you recognize the car, the way I do?"

Experiments with experimental rule-based models to go along with imodels.

Initial Exploratory Works on Knowledge Tracing in Transformer Based Language Models

Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations

CVPR 2021 | Metrics for evaluating interpretability methods.

🦠 DeepDecipher: An open source API to MLP neurons

Code for the paper "Comparing Feature Importance and Rule Extraction for Interpretability on Text Data", XAIE @ ICPR 2022

Metrics for evaluating interpretability methods.

This project focuses on modeling auto encoder-based architectures for unsupervised detection of anomalies in 12-lead electrocardiograms (ECGs)

A repository to study the interpretability of time series networks(LSTM)

Repository for the journal article 'SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction'

A curated list of awesome machine learning interpretability resources.