model-interpretability | Topic | Ecosyste.ms: Repos

Topic: "model-interpretability"

kserve/kserve

Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes

Language: Python - Size: 430 MB - Last synced at: 3 days ago - Pushed at: 7 days ago - Stars: 4,508 - Forks: 1,242

kitops-ml/kitops

An open source DevOps tool for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI artifact.

Language: Go - Size: 49.3 MB - Last synced at: 22 days ago - Pushed at: 22 days ago - Stars: 1,139 - Forks: 129

yizt/Grad-CAM.pytorch

pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...

Language: Python - Size: 2.25 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 695 - Forks: 165

yulongwang12/visual-attribution

Pytorch Implementation of recent visual attribution methods for model interpretability

Language: Jupyter Notebook - Size: 27.7 MB - Last synced at: almost 2 years ago - Pushed at: over 5 years ago - Stars: 141 - Forks: 25

rehmanzafar/xai-iml-sota

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

Language: R - Size: 1.12 MB - Last synced at: over 2 years ago - Pushed at: almost 3 years ago - Stars: 51 - Forks: 13

TannerGilbert/Model-Interpretation

Overview of different model interpretability libraries.

Language: Jupyter Notebook - Size: 19.8 MB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 38 - Forks: 13

Hellisotherpeople/Active-Explainable-Classification

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

Language: HTML - Size: 2.78 MB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 29 - Forks: 1

Tramac/pytorch-cam

Class Activation Map (CAM) Visualizations in PyTorch.

Language: Python - Size: 1.42 MB - Last synced at: over 2 years ago - Pushed at: over 5 years ago - Stars: 29 - Forks: 4

evanseitz/squid-nn

surrogate quantitative interpretability for deepnets

Language: Python - Size: 3.83 MB - Last synced at: 26 days ago - Pushed at: 26 days ago - Stars: 24 - Forks: 1

pranoy-panda/Causal-Feature-Subset-Selection

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

Language: Python - Size: 178 KB - Last synced at: 8 months ago - Pushed at: over 4 years ago - Stars: 18 - Forks: 4

zphang/saliency_investigation

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

Language: Python - Size: 293 KB - Last synced at: 5 months ago - Pushed at: almost 5 years ago - Stars: 13 - Forks: 5

AmanPriyanshu/GPT-OSS-MoE-ExpertFingerprinting

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

Language: HTML - Size: 127 MB - Last synced at: 9 days ago - Pushed at: 24 days ago - Stars: 10 - Forks: 1

aimaster-dev/default_loan_prediction

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.

Language: JavaScript - Size: 2.82 MB - Last synced at: 2 months ago - Pushed at: 3 months ago - Stars: 9 - Forks: 1

Ankit-Kumar-Saini/Coursera_TensorFlow_Advanced_Techniques_Specialization

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.

Language: Jupyter Notebook - Size: 53.9 MB - Last synced at: over 2 years ago - Pushed at: about 4 years ago - Stars: 9 - Forks: 4

Shuyib/chronic-kidney-disease-kaggle

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.

Language: Jupyter Notebook - Size: 3.78 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 8 - Forks: 1

nyuvis/ml-interview-study

Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs

Size: 341 KB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 7 - Forks: 2

dtheod/Interpretability-Methods-Regression

A python script for basic data cleaning/manipulation and modelling based on the open source House Sales Advanced Regression Techniques(Kaggle)

Language: Jupyter Notebook - Size: 559 KB - Last synced at: 4 months ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 0

YusufCaymazZ/Meovis

Meovis is an open-source tool that helps you visualize, explain, and compare your machine learning models with clarity — wrapped in a sleek, mascot-powered interface.

Language: TypeScript - Size: 6.84 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 2 - Forks: 0

Shelton-beep/predicting-gpa-using-lifestyle-factors

Predicting student GPA using lifestyle factors like study habits, sleep, and stress levels. A machine learning model built to help students and educators understand the impact of lifestyle choices on academic performance.

Language: Jupyter Notebook - Size: 404 KB - Last synced at: 6 months ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0

ondrejhruby/airbnb-analysis-machine-learning

A comprehensive end-to-end machine learning project analyzing Airbnb listings data. This project includes exploratory data analysis, model training, optimization, and model interpretability, using a randomly generated dataset for demonstration purposes.

Language: HTML - Size: 12.8 MB - Last synced at: about 2 months ago - Pushed at: 12 months ago - Stars: 1 - Forks: 0

evanseitz/squid-manuscript

squid repository for manuscript analysis

Language: Python - Size: 165 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

Machine-Learning-Foundations/day_14_exercise_interpretability

Exercise on interpretability with integrated gradients.

Language: Python - Size: 28.5 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 1

dg1223/explainable-ai

Model interpretability for Explainable Artificial Intelligence

Language: Jupyter Notebook - Size: 50.8 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

vafaei-ar/keras-translator

The "keras-translator" helps you to understand a keras trained model.

Language: Jupyter Notebook - Size: 440 KB - Last synced at: 7 months ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0

andreafortini/gradcam-tf2

Implementation of the Grad-CAM algorithm in an easy-to-use class, optimized for transfer learning projects and written using Keras and Tensorflow 2.x

Language: Python - Size: 937 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 1

diwakar-vsingh/Integrated-Gradients

An Explainable AI technique introduced in the paper Axiomatic Attribution for Deep Networks

Language: Jupyter Notebook - Size: 62.4 MB - Last synced at: over 2 years ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 1

nanjala116/unsupervised-island

🏕️ Trapped on a mysterious island with nothing but your machine learning skills and a handful of cryptic datasets, you must decode the flora, predict what’s safe to eat, and engineer your way to survival. This is more than a challenge—it's a data-driven fight for life.

Size: 94.7 KB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 0 - Forks: 0

Harold2701/ai-engineer-training

🚀 Enhance your AI skills with weekly lessons, code samples, and projects in this comprehensive training hub for aspiring AI engineers.

Language: Python - Size: 200 KB - Last synced at: 17 days ago - Pushed at: 17 days ago - Stars: 0 - Forks: 0

Amaan-developpeur/LiverDiseasePrediction

A machine learning–based application for early detection of liver disease using patient health parameters. The system preprocesses clinical data, trains a predictive model with scikit-learn, and integrates LIME for model explainability. A FastAPI backend exposes RESTful endpoints for predictions and interpretability reports.

Language: Jupyter Notebook - Size: 1.8 MB - Last synced at: 22 days ago - Pushed at: 22 days ago - Stars: 0 - Forks: 0

IDT-ITI/P-TAME

Scripts and trained models from our paper: M. Ntrougkas, V. Mezaris, I. Patras, "P-TAME: Explain Any Image Classifier with Trained Perturbations", IEEE Open Journal of Signal Processing, 2025. DOI:10.1109/OJSP.2025.3568756.

Language: Python - Size: 152 KB - Last synced at: 13 days ago - Pushed at: 25 days ago - Stars: 0 - Forks: 0

sushma-prog/Advanced-ML

A 7-day hands-on journey into advanced ML techniques including XGBoost, LightGBM, SHAP, LIME, GridSearchCV, and API integration.

Language: Jupyter Notebook - Size: 1.8 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

ryuzaki-ved/ckd_detection

This project focuses on predicting Chronic Kidney Disease (CKD) using neural networks. It involves comprehensive data preprocessing, exploratory data analysis, and model interpretability to provide a robust classification solution based on health records.

Language: Jupyter Notebook - Size: 57.3 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

muhammadhassaan-solves/interpreting-deep-learning-model-for-fairness-using-shap

Language: Python - Size: 5.86 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

vinit714/Player-Retention-Analysis

A complete Streamlit + Machine Learning + SHAP + NLP project to analyze, predict, and improve player retention in games. This project simulates a game environment, models churn behavior, and provides insights using SHAP, NLP word clouds, and strategy simulators.

Language: Python - Size: 207 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

david-xander/visual-analytics-tool-sentence-embeddings

A visual analytics tool and framework for exploring compositionality in sentence embeddings. Gain interactive insights into how embedding models, composition functions, and similarity metrics influence textual representations, focusing on error gap analysis for enhanced model interpretability.

Language: Python - Size: 13.7 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Chetnas8/acaml-web-app

ACAML is an Adaptive Constraint-Aware AutoML web app built with Streamlit. It automatically selects the best model for regression or classification tasks using FLAML, displays performance metrics, and provides SHAP-based feature explanations. Empower users to run and interpret ML models easily.

Language: Python - Size: 443 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

AdityaSreevatsaK/DAX-Framework-XAI

The DAX (Domain-Aligned Explainability) Framework is a practical guide for selecting Explainable AI (XAI) techniques tailored to real-world application needs. Rather than organizing XAI tools by algorithm class, DAX aligns method selection with Data Modality, Domain Constraints & Explanation Goals

Language: Python - Size: 12.7 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

SuryaVamsi-P/Loan-Default-Prediction-System-Flask-ML

Built and deployed a Flask-based machine learning system to predict loan default risk using customer demographics and financial indicators. Applied advanced ensemble models like XGBoost and LightGBM to achieve ~99% accuracy. Designed a full-stack solution with real-time prediction capabilities, enabling faster, smarter loan decisions in banking.

Language: Python - Size: 185 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

patricksferraz/pcr-analysis

Machine learning-powered PCR data analysis toolkit featuring transfer learning, time series forecasting, and SHAP-based model interpretability. Built with TensorFlow and scikit-learn for advanced biological data processing.

Language: Jupyter Notebook - Size: 9.66 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

RufelleEmmanuelPactol/16PF-Analysis-Deep-Ensemble-Learning

[Published, Macau, ICETT] EDA, and model development of utilizing 16 PF data for decision support systems in higher-level education. Utilizes "Augmented Clustering" and "Deep Ensemble Learning"

Language: Jupyter Notebook - Size: 54.1 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

Parisaroozgarian/model-interpretability

🎯 Deep Learning Model Analysis Made Easy: Visualize and understand your model's behavior, attention patterns, and decision boundaries with interactive visualizations.

Language: Python - Size: 0 Bytes - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

manmeetkaurbaxi/Loan-Default-Prediction

Analyze borrower data and enhance decision-making for financial institutions, focusing on mitigating risks, ensuring fairness, and maintaining transparency and regulatory compliance.

Language: Jupyter Notebook - Size: 63.4 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

Shubbair/Grad-CAM-animate

implementation of Grad-CAM as animate conv-layers

Language: Jupyter Notebook - Size: 2.47 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

AKKI0511/Masked-Language-Model

Powerful Python tool for visualizing and interacting with pre-trained Masked Language Models (MLMs) like BERT. Features include self-attention visualization, masked token prediction, model fine-tuning, embedding analysis with PCA/t-SNE, and SHAP-based model interpretability.

Language: Python - Size: 122 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

Mattjesc/Federated-Learning-Simulation-1GPU-MI-IS

Federated Learning Simulation on a Single GPU with Model Interpretability and Interactive Visualization

Language: Python - Size: 15.6 KB - Last synced at: 6 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

IDT-ITI/T-TAME

Scripts and trained models from our paper: M. Ntrougkas, N. Gkalelis, V. Mezaris, "T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers", IEEE Access, 2024. DOI:10.1109/ACCESS.2024.3405788.

Language: Jupyter Notebook - Size: 10.7 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

CSingh26/Project3-SentimentAnalysis

Sentiment Analysis using Machine Learning

Language: Python - Size: 769 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

Abhinav1004/Coding-Assesment-Tool

To predict the rating of a developer using various data captured during an online test

Language: Jupyter Notebook - Size: 4.11 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

SharathHebbar/Softmax-as-intermediate-layer-CNN

Softmax-as-intermediate-layer-CNN

Language: Jupyter Notebook - Size: 1.69 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

lukasgabor/SDMs-affected-by-positional-uncertainty-in-occurrences-can-still-be-ecologically-interpretable

This repository provides R scripts for reproducing virtual species generating, modeling species distribution and final figures related with published manuscript.

Language: R - Size: 49.8 KB - Last synced at: over 1 year ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

ataozarslan/PyCaret_Tutorial

This repository includes a general informations and examples about how to make a machine learning model just a few lines of code in Python using PyCaret package.

Language: Jupyter Notebook - Size: 130 KB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

sanketsanap5/Bank-Loan-Status-Predictive-Analysis

erformed a predictive analysis on the customer's Bank Loan Application data to predict loan status. Using python, pandas, scipy, seaborn, AutoML libraries, and machine learning techniques. Used Machine Learning techniques to accurately predict the evaluation scheme if the particular loan will be 'Fully Paid' or 'Charged Off'. This means if Bank accepts a particular person's loan application will it be 'Fully Paid' or 'Charged Off'

Language: Jupyter Notebook - Size: 19 MB - Last synced at: over 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

cedrickchee/anchor Fork of marcotcr/anchor

Code for "High-Precision Model-Agnostic Explanations" paper. A follow up to LIME model.

Language: Jupyter Notebook - Size: 7.52 MB - Last synced at: over 1 year ago - Pushed at: almost 7 years ago - Stars: 0 - Forks: 0