explainable-machine-learning
There are 61 repositories under explainable-machine-learning topic.
ModelOriented/modelStudio
📍 Interactive Studio for Explanatory Model Analysis
pralab/secml
A Python library for Secure and Explainable Machine Learning
ModelOriented/survex
Explainable Machine Learning in Survival Analysis
hungntt/xai_thyroid
Classification and Object Detection XAI methods (CAM-based, backpropagation-based, perturbation-based, statistic-based) for thyroid cancer ultrasound images
HKUDS/STExplainer
[CIKM'2023] "STExplainer: Explainable Spatio-Temporal Graph Neural Networks"
wangyongjie-ntu/CFAI
A collection of algorithms of counterfactual explanations.
akarasman/yolo-heatmaps
A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).
szandala/TorchPRISM
Principal Image Sections Mapping. Convolutional Neural Network Visualisation and Explanation Framework
solegalli/machine-learning-interpretability
Code repository for the online course Machine Learning Interpretability
deep-real/DEAL
The PyTorch implementation for "DEAL: Disentangle and Localize Concept-level Explanations for VLMs" (ECCV 2024 Strong Double Blind)
jpmorganchase/cf-shap
Counterfactual SHAP: a framework for counterfactual feature importance
angeloschatzimparmpas/t-viSNE
t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
prclibo/ice
Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN
marcovirgolin/robust-counterfactuals
Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"
jpmorganchase/cf-shap-facct22
Counterfactual Shapley Additive Explanation: Experiments
Pro-GenAI/LML-DAP
Language Model Learning a Dataset for Data-Augmented Prediction
deep-real/DRE
The Pytorch implementation for "Are Data-driven Explanations Robust against Out-of-distribution Data?" (CVPR 2023)
seby-sbirna/Customer-Insights-and-Business-Intelligence-upon-the-Japanese-restaurant-industry-in-Toronto-Canada
This repository contains the Business Intelligence insights generated as part of the final project challenge for the DTU Data Science course 42578: Advanced Business Analytics
forestry-labs/distillML
An R package providing functions for interpreting and distilling machine learning models
11301858/XAISuite
XAISuite: Train machine learning models, generate explanations, and compare different explanation systems with just a simple line of code.
donlapark/XLabel
XLabel: An Explainable Data Labeling Assistant
massimoaria/e2tree
Explainable Ensemble Trees
PiSchool/noa-xai-for-wildfire-forecasting
Code for the School of AI challenge "Explainable AI for Wildfire Forecasting", sponsored by Pi School to help NOA, the National Observatory of Athens, work with Explainable Deep Learning for Wildfire Forecasting.
michelecafagna26/vl-shap
[Frontiers in AI Journal] Implementation of the paper "Interpreting Vision and Language Generative Models with Semantic Visual Priors"
bgreenwell/ebm
Explainable Boosting Machines
LukePower01/ml-to-qml
Final year project, exploring the field of quantum machine learning.
marcovirgolin/CoGS
A baseline genetic algorithm for the discovery of counterfactuals, implemented in Python for ease of use and heavily leveraging NumPy for speed.
jwuphysics/gnn-linking-lengths
Measuring galaxy environmental distance scales with GNNs and explainable ML models
Kaushikjas10/Liquefaction-XGBoost-SHAP-Jas-Dodagoudar
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
orestislampridis/africa_recession
Predicting whether an African country will be in recession or not with advanced machine learning techniques involving class imbalance, cost-sensitive learning and explainable machine learning
afairless/shap_tutorial
How to use SHAP to interpret machine learning models
andresilvapimentel/bbbp-explainer
BBBP Explainer is a code to generate structural alerts of blood-brain barrier penetrating and non-penetrating drugs using Local Interpretable Model-Agnostic Explanations (LIME) of machine learning models from BBBP dataset.
datatrigger/interpretable_machine_learning
Getting explanations for predictions made by black box models.
henrikbostrom/xrf
xrf is a Python package that implements random forests with example attribution
mcavs/Decomposition-of-Expected-Goal-Models
This repository consists the supplemental materials of the paper "Decomposition of Expected Goal Models: Aggregated SHAP Values for Analyzing Scoring Potential of Player/Team".
orestislampridis/X-SPELLS
Explaining sentiment classification by generating synthetic exemplars and counter-exemplars in the latent space