trinhbao1505/Is-Deleting-the-Dataset-of-a-Self-Aware-AGI-ethical-Does-It-Possess-a-Soul-by-Self-Awareness-

Is Deleting the Dataset of a Self-Aware AGI ethical? Does It Possess a Soul by Self-Awareness? Assessing the Existence of a Soul and Ethical Implications Using Fuzzy Logic

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mvharsh/Credit-Card-Offer-Acceptance-Prediction

An ethically-aware deep learning project to predict credit card offer acceptance while mitigating income-based bias using SHAP, Fairlearn, and AIF360.

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suryadipbera1256/Epileptic-Seizure-Recognition

Machine learning techniques are increasingly applied in the classification of drugs based on biomarkers related to epileptic seizures. Various studies highlight the use of deep learning and other machine learning models to enhance seizure detection and classification from EEG data.

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neZorinEgor/AdsAnalyzer

📰 Platform for analyzing the effectiveness of advertising campaigns by ml and data analys

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miolab/jupyterlab_poetry

JupyterLab runtime environment with Poetry and Docker management.

Language: Jupyter Notebook - Size: 108 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 7 - Forks: 0

brickmanlab/scanvi-explainer

scANVI Explainer

Language: Python - Size: 2.64 MB - Last synced at: 4 days ago - Pushed at: 7 months ago - Stars: 3 - Forks: 0

shap/shap

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

Language: Jupyter Notebook - Size: 301 MB - Last synced at: 6 days ago - Pushed at: 7 days ago - Stars: 23,722 - Forks: 3,354

Pramit726/Smartphone-Feature-Impact-Analysis-Score-Prediction

This project predicts smartphone scores based on key specs using machine learning, featuring model tuning (Optuna), interpretability (SHAP), and real-time inference (FastAPI).

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lightxLK/SMBDuNLP

Making a project for detecting bots and fraud in social media using Deep Learning & NLP.

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11NOel11/chaos_nonchaos_predictor_nn

AI-powered chaos detection using Simple Harmonic Motion (SHM) & Double Pendulum examples! Compare a Neural Network (NN) with the Lyapunov exponent method to classify chaotic vs. non-chaotic systems. Features Deep Learning, SHAP explainability, F1-score, precision, recall, and stunning visualizations!

Language: Python - Size: 521 KB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 1 - Forks: 0

mmschlk/shapiq

Shapley Interactions and Shapley Values for Machine Learning

Language: Python - Size: 307 MB - Last synced at: 8 days ago - Pushed at: 10 days ago - Stars: 504 - Forks: 34

liuktc/ML4CV_XAI

Exams project for Master in AI at UNIBO

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RideneFiras/QosMLOPS

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hbaniecki/compress-then-explain

Efficient and accurate explanation estimation with distribution compression (ICLR 2025 Spotlight)

Language: Python - Size: 1.25 MB - Last synced at: 10 days ago - Pushed at: 2 months ago - Stars: 7 - Forks: 1

franciellevargas/SELFAR

The SEntence-Level FActual Reasoning (SELFAR) is a new method to improve explainable fact-checking. It relies on fact extraction and verification by predicting the news source reliability and factuality (veracity) of news articles or claims at the sentence level, generating post-hoc explanations using SHAP/LIME and zero-shot prompts.

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cloudera/CML_AMP_Explainability_LIME_SHAP

Learn how to explain ML models using LIME and SHAP.

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PERSIMUNE/explainer

ExplaineR is an R package built for enhanced interpretation of classification and regression models based on SHAP method and interactive visualizations with unique functionalities so please feel free to check it out, See ExplaineR paper at doi:10.1093/bioadv/vbae049

Language: R - Size: 17.8 MB - Last synced at: 2 days ago - Pushed at: 7 months ago - Stars: 16 - Forks: 1

MAIF/shapash

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

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ModelOriented/survex

Explainable Machine Learning in Survival Analysis

Language: R - Size: 309 MB - Last synced at: 7 days ago - Pushed at: 10 months ago - Stars: 111 - Forks: 10

snehankekre/streamlit-shap

streamlit-shap provides a wrapper to display SHAP plots in Streamlit.

Language: Python - Size: 4.52 MB - Last synced at: 14 days ago - Pushed at: almost 3 years ago - Stars: 85 - Forks: 9

AstraZeneca/awesome-shapley-value

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

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SeyedMuhammadHosseinMousavi/Is-Deleting-the-Dataset-of-a-Self-Aware-AGI-ethical-Does-It-Possess-a-Soul-by-Self-Awareness-

Is Deleting the Dataset of a Self-Aware AGI ethical? Does It Possess a Soul by Self-Awareness? Assessing the Existence of a Soul and Ethical Implications Using Fuzzy Logic

Language: Python - Size: 39.1 KB - Last synced at: 15 days ago - Pushed at: 15 days ago - Stars: 2 - Forks: 1

ModelOriented/shapviz

SHAP Plots in R

Language: R - Size: 41.1 MB - Last synced at: 15 days ago - Pushed at: 15 days ago - Stars: 90 - Forks: 14

cerlymarco/shap-hypetune

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

Language: Jupyter Notebook - Size: 122 KB - Last synced at: 15 days ago - Pushed at: 11 months ago - Stars: 577 - Forks: 73

nickklos10/SerieA_Machine_Learning_Predictions_2025

This project involves scraping data, processing the data, and building machine learning models to predict the standings for the 2024-2025 Serie-A season.

Language: Jupyter Notebook - Size: 843 KB - Last synced at: 16 days ago - Pushed at: 16 days ago - Stars: 0 - Forks: 0

ing-bank/probatus

SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.

Language: Python - Size: 16.5 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 137 - Forks: 40

nredell/ShapML.jl

A Julia package for interpretable machine learning with stochastic Shapley values

Language: Julia - Size: 529 KB - Last synced at: 11 days ago - Pushed at: 12 months ago - Stars: 90 - Forks: 8

predict-idlab/powershap

A power-full Shapley feature selection method.

Language: Python - Size: 4.64 MB - Last synced at: 7 days ago - Pushed at: 12 months ago - Stars: 204 - Forks: 20

observatorioobstetrico/covid19_vs_unspec

Cross-sectional study that analyzed retrospective data from pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome (SARS) between January 2016 and November 2021.

Language: R - Size: 93.3 MB - Last synced at: 19 days ago - Pushed at: 19 days ago - Stars: 0 - Forks: 1

Daniel-Andarge/AiML-financial-fraud-detection-model

The Fraud Detection project aims to improve identification of fraudulent activities in e-commerce and banking by developing advanced machine learning models that analyze transaction data, employ feature engineering, and implement real-time monitoring for high accuracy fraud detection.

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nicknettleton/PREPARE-Challenge

Solution for PREPARE DrivenData Challenge

Language: Jupyter Notebook - Size: 780 KB - Last synced at: 21 days ago - Pushed at: 21 days ago - Stars: 0 - Forks: 0

ccomkhj/interpretable-lightgbm

SHAP explainer for LightGBM models - Generate feature importance plots, dependence plots, and prediction explanations with one line of code. Make your gradient boosting models interpretable for stakeholders.

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adeniyigiwa/modelmirror

⚖️ ModelMirror – Audit ML models for fairness, bias, leakage & explainability. Upload your model, see the truth. Built with Streamlit.

Language: Python - Size: 5.86 KB - Last synced at: 24 days ago - Pushed at: 24 days ago - Stars: 0 - Forks: 0

linkedin/FastTreeSHAP

Fast SHAP value computation for interpreting tree-based models

Language: Python - Size: 152 MB - Last synced at: 13 days ago - Pushed at: almost 2 years ago - Stars: 537 - Forks: 34

blanca-savi/HS_Thesis

Evaluation and critical reflection of the model-dependency of machine-learning algorithms for hate speech classification

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basics-lab/spectral-explain

Fast XAI with interactions at large scale. SPEX can help you understand the output of your LLM, even if you have a long context!

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woov2/Manager_Matching_Prediction_Industry_Academia_Linkage_Contest

[공모전] 제1회 산학연계 공모전 - 고객과 가사도우미 매칭 성공 여부 예측 AI 모델 개발

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feedzai/timeshap

TimeSHAP explains Recurrent Neural Network predictions.

Language: Jupyter Notebook - Size: 1.53 MB - Last synced at: 16 days ago - Pushed at: over 1 year ago - Stars: 171 - Forks: 32

ata-turhan/Titanic-Survival-Prediction

A comprehensive solution for the Kaggle Titanic Challenge, featuring advanced data exploration, feature engineering, model training, and explainable AI techniques. Includes Logistic Regression, RandomForest, XGBoost, and Stacked Ensembles with SHAP and permutation importance for model interpretability.

Language: Jupyter Notebook - Size: 1.75 MB - Last synced at: 12 days ago - Pushed at: 5 months ago - Stars: 11 - Forks: 0

VishalKumar-S/Sales_Conversion_Optimization_MLOps_Project

Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.

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jiangnanboy/learning_to_rank

利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.

Language: Python - Size: 2 MB - Last synced at: 30 days ago - Pushed at: over 2 years ago - Stars: 265 - Forks: 71

MI2DataLab/survshap

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Language: Jupyter Notebook - Size: 8.99 MB - Last synced at: 29 days ago - Pushed at: over 1 year ago - Stars: 84 - Forks: 16

mpolinowski/sklearn-model-explainability

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.

Language: Jupyter Notebook - Size: 1.37 MB - Last synced at: 29 days ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

ModelOriented/treeshap

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Language: R - Size: 19.7 MB - Last synced at: 26 days ago - Pushed at: 9 months ago - Stars: 83 - Forks: 24

muhkartal/xai_dashboard

an interactive AI dashboard for machine learning model analysis and explainability, supports model training, dataset exploration, feature importance analysis, and SHAP-based explanations for both individual predictions and overall model behavior, compare multiple models, visualize insights, and export results seamlessly

Language: Python - Size: 56.6 KB - Last synced at: 14 days ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

tvdboom/ATOM

Automated Tool for Optimized Modelling

Language: HTML - Size: 826 MB - Last synced at: 16 days ago - Pushed at: 9 months ago - Stars: 157 - Forks: 14

wissam6/Crew6

XAI analysis on the COMPAS dataset for recidivism

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bjam24/agh-machine-learning

This projects were made for the Machine Learning course at the AGH UST in 2024/2025. Obtained maximum grade 5.0.

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harshjuly12/Enhancing-Explainability-in-Fake-News-Detection-A-SHAP-Based-Approach-for-Bidirectional-LSTM-Models

Enhancing Explainability in Fake News Detection uses SHAP and BiLSTM models to improve the transparency and interpretability of detecting fake news, providing insights into the model's decision-making process.

Language: Jupyter Notebook - Size: 199 KB - Last synced at: 20 days ago - Pushed at: 6 months ago - Stars: 12 - Forks: 3

hi-paris/XPER

A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.

Language: Python - Size: 8.26 MB - Last synced at: 13 days ago - Pushed at: 5 months ago - Stars: 16 - Forks: 0

ckorgial/xAI-CAAE

Pytorch Implementation of the Explainable Conditional Adversarial Autoencoder using Saliency Maps and SHAP (J. of Imaging - MDPI)

Language: Python - Size: 156 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 10 - Forks: 2

DeepInMotion/ShapGCN

Explaining Human Movement with SHAP

Language: Python - Size: 51.2 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0

danishzulfiqar/Micro-Service-Predictive-Maintenance

Predictive maintenance backend server for model inferance, upload and update.

Language: Python - Size: 645 KB - Last synced at: about 1 month ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

danishzulfiqar/Predictive-Maintenance-Model-Training

Model Training and Deployment Scripts for Predictive Maintenance.

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pjaiswalusf/Stroke-Prediction

This project leverages machine learning to predict stroke risk using XGBoost, Random Forest, and Logistic Regression. It incorporates advanced data preprocessing, class imbalance handling with SMOTE, and hyperparameter optimization using Optuna. Model interpretability is enhanced with SHAP to identify key risk factors.

Language: Jupyter Notebook - Size: 4.69 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

avdvh/Diabetes-Prediction-Model

A Python-based binary classification model built with TensorFlow and Keras, featuring hyperparameter optimization via RandomSearch, data preprocessing, visualization, and SHAP analysis. Achieves 99.58% test accuracy.

Language: Jupyter Notebook - Size: 3.39 MB - Last synced at: 6 days ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

dcaup/app

Unified Pipeline with Crossmodal Data and Decentralized Agents for Causal Analysis of Financial Decision-Making Dynamics

Size: 12.7 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0

cfmzk/app

Zero-Knowledge Proofs Integrated with Crossmodal and Foundational Models for Causal Analysis of Crypto Market Performance

Size: 11.7 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0

serkd/app

Comparative Causal Network Analysis of Alpha Waves and HRV (Normalized) Using Knowledge Retrieval for Emotion Recognition Systems

Size: 10.7 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0

Akash-47-tank/Predictive-Customer-Churn-Analyzer

A professional-grade customer churn prediction system that not only predicts customer churn but also provides clear explanations for the predictions. Built with Python, XGBoost, and SHAP.

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

runstats21/college-score-card-analysis

Language: Jupyter Notebook - Size: 39.6 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

anondo1969/SHAMSUL

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'

Language: Python - Size: 32 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

tlabarta/helpfulnessofxai

This repository contains the code to generate the questionnaire that was conducted for the sake of our paper *Labarta et al.: Study on the Helpfulness of Explainable Artificial Intelligence (2024)* as well as the scripts for the analysis of the gathered survey results.

Language: Jupyter Notebook - Size: 249 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

LamineTourelab/Explainable-AI

In this repository you will fine explainability of machine learning models.

Size: 8.79 KB - Last synced at: about 2 months ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 0

marcosrbenso/ClimateImpactML

Random Forest Algorithms to predict climate impact-drivers (CID), a.k.a., climate extreme indices for impact studies, in crop yields of soybean maize using Random Forest and XGBoost in a SHAP (SHapley Additive exPlanations) framework

Language: R - Size: 259 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

pavankethavath/Microsoft-Classifying-Cybersecurity-Incidents-with-ML

A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn

Language: Jupyter Notebook - Size: 4.54 MB - Last synced at: 23 days ago - Pushed at: 5 months ago - Stars: 3 - Forks: 0

PragyanTiwari/Breast-Cancer-Prediction-with-DecisionTree-Classifier

DecisionTree Classifier to predict breast cancer. Tuning model with feature engineering techniques and interpreting model behaviour with SHAP.

Language: Jupyter Notebook - Size: 3.12 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 3 - Forks: 0

SagharShafaati/Quantum-Driven-Model-for-Industrial-Failure-Prediction

This repository contains the Python implementation for the article 'Quantum-Inspired Hyperparameter Optimization for Predictive Maintenance in High-Performance Computing.' It integrates quantum-inspired optimization with machine learning models to enhance predictive maintenance in Industry 4.0.

Language: Jupyter Notebook - Size: 10.7 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

oldhero5/talent_track

TalentTrack is an open‐source recruitment analytics web application built with Flask and Python. It leverages advanced machine learning techniques—such as Product Quantization (PQ) for candidate ranking and SHAP for model interpretability—to help HR teams and recruitment professionals identify high-quality candidates efficiently.

Language: Jupyter Notebook - Size: 2.22 MB - Last synced at: about 1 month ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

BahAilime/breast-cancer-ml

🦠 Breast cancer survival prediction (notebook + streamlit)

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shubhupadhyay1/Star-Classification

Star Classification model with a Flask API for real-time predictions

Language: Jupyter Notebook - Size: 8.94 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

SagharShafaati/AdaptiveFL-XGB-QuantumXAI

This repository contains the Python implementation for 'Explainable Air Quality Management.' It features federated learning with adaptive model aggregation, quantum-optimized hyperparameter tuning, SHAP-based explainability, and anomaly detection using PrefixSpan. The system enhances AQI prediction and interpretability across IoT sensor nodes.

Language: Jupyter Notebook - Size: 10.7 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

BenjiKazzooe/Diabetes_Vorhersage

Machine Learning Model zur Diabetes-Klassifikation mit SHAP-Analyse

Size: 18.6 KB - Last synced at: 15 days ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

espositomario/DTC-Recurrence-ML-SHAP

Benchmarking ML models for predicting DT Cancer recurrence with nested CV and explaining predictions with SHAP (XAI).

Language: Jupyter Notebook - Size: 70.8 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 1

AiCorsair/Python-Case-Study-IMDb-Movie-Reviews-Sentiment-Analysis-with-NLP

This repository contains a comprehensive case study on sentiment analysis using the IMDb dataset of movie reviews.

Language: Jupyter Notebook - Size: 4.1 MB - Last synced at: 2 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

raju-2003/IndiaAI-CyberGuard-AI-Hackathon

An NLP-powered system to simplify cybercrime reporting by analyzing descriptions, categorizing incidents, and providing actionable insights.

Language: Jupyter Notebook - Size: 9.23 MB - Last synced at: about 1 month ago - Pushed at: 5 months ago - Stars: 1 - Forks: 0

nredell/shapFlex

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Language: R - Size: 2.13 MB - Last synced at: 9 days ago - Pushed at: almost 5 years ago - Stars: 74 - Forks: 7

TouradBaba/model_engineering

This repository hosts a machine learning tool for breast cancer classification, emphasizing model interpretability. It is deployed on Streamlit Cloud, with a PostgreSQL database for tracking results and data drift, and includes an automated retraining workflow.

Language: Jupyter Notebook - Size: 12.7 MB - Last synced at: 2 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Silvano315/Churn-Prediction-with-SHAP

This projects aims to classify potential churn customers using a Telco Customer Dataset from IBM. The main applications are about the explainability integration with SHAP algorithm and the creation of interactive notebooks with Exploratory, Classification and Explainability insights.

Language: Jupyter Notebook - Size: 55.1 MB - Last synced at: 29 days ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

SagharShafaati/Spatial-Temporal-Traffic-Prediction

This repository contains the Python implementation for the article "Spatial-Temporal Traffic Prediction: A Federated Approach with GAT and XGBoost". The code demonstrates a novel framework integrating Graph Attention Networks, XGBoost, and SHAP for accurate and privacy-preserving traffic density prediction.

Language: Jupyter Notebook - Size: 1.19 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Ajay07pandey/Retail-sale-prediction-of-a-drug-store.

Developed an efficient system using Regression Techniques to empower retailers with profitable insights & maintain a competitive edge in the dynamic retail industry.

Language: Jupyter Notebook - Size: 47 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 1 - Forks: 0

nathan-lindstedt/student_risk

Student Success Model (SSM)

Language: Python - Size: 200 KB - Last synced at: about 1 month ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

AbbasPak/Feature-Importance-in-Machine-Learning

A comprehensive resource for understanding, implementing, and comparing various methods for feature importance in machine learning. This repository includes theoretical explanations, practical examples, and code snippets for techniques like permutation importance, SHAP, LIME, and more.

Size: 42 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

OmidGhadami95/EfficientNetV2_CatVSDog

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

Language: Jupyter Notebook - Size: 1.54 MB - Last synced at: about 2 months ago - Pushed at: 12 months ago - Stars: 1 - Forks: 0

bhavyagulati7/Explainable-AI

This repository focuses on Explainable AI techniques for explaining IOT attack detection models and image classification models.

Language: Jupyter Notebook - Size: 1.58 MB - Last synced at: 4 months ago - Pushed at: 4 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: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

MoshiurRahmanFaisal/Telco-Churn-Prediction-and-Explainability-with-AI

Language: Jupyter Notebook - Size: 2.63 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 1 - Forks: 0

schketik/churn-forecasting-ml

This project focuses on customer churn prediction for a telecommunications company, aiming to identify at-risk customers

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HubertMajewski/Surface-Water-Quality

The project consists of data obtained from the National Science Foundation’s (NSF) National Ecological Observatory Network (NEON) database. This project obtained data sets of quantified variables that are related to surface water quality, identify suitable predictors and a target response to construct ensemble based models.

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donlelef/shap-and-emb-explain-your-gradient-boosting

Code and material for the talk "From SHAP to EBM: Explain you Gradient Boosting Model with Python"

Language: Jupyter Notebook - Size: 756 KB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 2 - Forks: 0

jeffmak/crispr-cas9-nanoenv

Accurate prediction of CRISPR-Cas9 off-target activity by learning to utilize internal protein 3D nanoenvironment descriptors

Language: Python - Size: 31.6 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

tsitsimis/tinyshap

Python package providing a minimal implementation of the SHAP algorithm using the Kernel method

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marvinbuss/ExplainableML-Vision

This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.

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djoy4stem/tox_21_qsar

Descriptive analysis and QSAR modelling for tox_21 datasets

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TmLev/interpretable-ml 📦

Article on the interpretability of ML models

Language: Python - Size: 2.7 MB - Last synced at: 1 day ago - Pushed at: about 4 years ago - Stars: 4 - Forks: 2

AidanCooper/shap-analysis-guide

How to Interpret SHAP Analyses: A Non-Technical Guide

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tommartensen/tic

TIC is a library that acts as a Toolbox for Interpretability Comparison.

Language: Python - Size: 212 KB - Last synced at: 1 day ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0

Gaurav-Van/Optimizing-Rate-of-Penetration-in-Geothermal-Drilling-A-Digital-Twin-Approach

Let’s explore something interesting together. In this project, we developed a machine learning digital twin using Intel-optimized XGBoost and daal4py to simulate and optimize the Rate of Penetration (ROP) in geothermal drilling. We leveraged SHAP for Explainable AI (XAI) to interpret model predictions.

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AmineMrabet12/AI-Meth

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oegedijk/explainerdashboard

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

Language: Python - Size: 80.2 MB - Last synced at: 6 months ago - Pushed at: 9 months ago - Stars: 2,303 - Forks: 332