model-interpretability

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

machine-learning human-in-the-loop interpretability explainable-artificial-intelligence researchers interactive-machine-learning deep-learning-visualization human-in-the-loop-machine-learning explainable-ml xai interpretable interpretable-machine-learning iml model-interpretability explainable interpretable-models explainable-models interpretable-learning explaining-ai explanation-methods

Updated Sep 29, 2022
R

TannerGilbert / Model-Interpretation

Sponsor

Star 50

Overview of different model interpretability libraries.

lime model-interpretation shap model-interpretability captum tf-explain

Updated Jul 2, 2022
Jupyter Notebook

Tramac / pytorch-cam

Star 31

Class Activation Map (CAM) Visualizations in PyTorch.

visualization pytorch class-activation-map model-interpretability

Updated May 20, 2020
Python

Hellisotherpeople / Active-Explainable-Classification

Star 29

A set of tools for leveraging pre-trained embeddings, active learning and model explainability for effecient document classification

python sklearn word-embeddings document-classification flair active-learning model-interpretation eli5 document-classifier model-interpretability

Updated Jan 23, 2025
HTML

evanseitz / squid-nn

Star 24

surrogate quantitative interpretability for deepnets

motif-analysis surrogate-modelling explainable-ai model-interpretability attribution-methods regulatory-genomics

Updated Aug 11, 2025
Python

pranoy-panda / Causal-Feature-Subset-Selection

Star 20

Official repository for the paper "Instance-wise Causal Feature Selection for Model Interpretation" (CVPRW 2021)

causality explainable-ml model-interpretability

Updated May 29, 2021
Python

zphang / saliency_investigation

Star 13

Code for "Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability" (https://arxiv.org/abs/2010.09750)

model-interpretability saliency-methods

Updated Nov 10, 2020
Python

AmanPriyanshu / GPT-OSS-MoE-ExpertFingerprinting

Sponsor

Star 12

ExpertFingerprinting: Behavioral Pattern Analysis and Specialization Mapping of Experts in GPT-OSS-20B's Mixture-of-Experts Architecture

deep-learning model routing moe pruning gpt reasoning mixture-of-experts model-interpretability behavioral-analysis pruning-optimization model-observability llm reasoning-agent llm-evaluation reasoning-language-models expert-routing gpt-oss gpt-oss-120b gpt-oss-20b

Updated Aug 13, 2025
HTML

aimaster-dev / default_loan_prediction

Star 12

This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.

flask finance automation vuejs sklearn fintech xgboost classification lightgbm banking-applications fraud-detection roc-auc credit-risk loan-prediction-analysis shap explainability model-interpretability loan-prediction

Updated May 29, 2025
JavaScript

juliotorrest / yield_aeroponics

Star 11

AI to Predict Yield in Aeroponics

ensemble-learning aeroponics precision-agriculture yield-prediction model-interpretability

Updated Sep 10, 2021
Jupyter Notebook

Ankit-Kumar-Saini / Coursera_TensorFlow_Advanced_Techniques_Specialization

Star 11

Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.

object-detection image-segmentation generative-adversarial-networks auto-encoders saliency-map mask-rcnn variational-autoencoders coursera-specialization retina-net model-interpretability tensorflow2 functional-api custom-model-development custom-loss-functions gradcam-visualization custom-layers gradient-tape-optimization custom-training-loops

Updated Jun 9, 2021
Jupyter Notebook

Shuyib / chronic-kidney-disease-kaggle

Star 8

Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.