GitHub topics: shap | Ecosyste.ms: Repos

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: 6 days 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

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.

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LennardZuendorf/thesis-webapp 📦

Webapp/Application implemention of my thesis about XAI and Interpretability of Transformer Models.

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Purushothaman-natarajan/VALE-Explainer

Language-Aware Visual Explanations (LAVE) is a framework designed for image classification tasks, particularly focusing on the ImageNet dataset. Unlike conventional methods that necessitate extensive training, LAVE leverages SHAP (SHapley Additive exPlanations) values to provide insightful textual and visual explanations.

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Silvano315/Stroke_Prediction

Stroke prediction with machine learning and SHAP algorithm using Kaggle dataset

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CyrilJl/apyxl

apyxl simplifies non-linear regressions/classifications and model explainability for all users

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raquelcolares/machine_learning_UFMG

My repository of Machine Learning by UFMG

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ozerzeynep/IstanbulEarthquake

GELİŞMİŞ ÖZELLİK MÜHENDİSLİĞİ VE MAKİNE ÖĞRENMESİ REGRESYON TEKNİKLERİ İLE DEPREM TAHMİNİ

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xplainable/xplainable

Real-time explainable machine learning for business optimisation

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Anotherafael/BankCustomerChurnPrediction

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Vanyaeremin/yandex_practicum_data_science

В данном репозитории хранятся выполненные мною проекты, в рамках обучения на курсе Яндекс. Практикума "Специалист по Data Science"

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akthammomani/AI_powered_heart_disease_risk_assessment_app

Build a Web App called AI-Powered Heart Disease Risk Assessment App

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epfl-ml4ed/evaluating-explainers

Comparing 5 different XAI techniques (LIME, PermSHAP, KernelSHAP, DiCE, CEM) through quantitative metrics. Published at EDM 2022.

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0eix/IBM-DS-SPACEX-FALCON9

IBM Professional data science certificate Final Project Notebooks

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MissMukuru/Telco_customer_churn

Capstone Project from Lux academy that utilizes all the concepts taught in the 5 week bootcamp.

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eXascaleInfolab/ImputeVIS

ImputeVIS - eXascale Infolab, University of Fribourg, Switzerland

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ataozarslan/Hotel_Cancellations

This repository includes a machine learning modeling study about estimating customers hotel cancellation and what are the reasons for these cancellations.

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HROlive/Introduction-to-Explainable-Deep-Learning-on-Supercomputers

A solid foundational understanding of XAI, primarily emphasizing how XAI methodologies can expose latent biases in datasets and reveal valuable insights.

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schketik/Market_Place_ML_best_model

Using ML models to personalize offers to loyal customers to increase their purchasing activity

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ataozarslan/streamlit_demo

This repository includes a Streamlit ML Classification project files. You can visit the website via the link below.

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AliAmini93/Telecom-Churn-Analysis

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

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anVSS1/PFE

This project dives deep into customer sales data to uncover valuable insights for business decision-making. It leverages machine learning and time-series forecasting to predict customer churn, forecast product demand, and segment customers based on their purchasing behavior.

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offthetab/ML_homework

Практические работы по анализу данных МИРЭА 3 курс.

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M-Fatoni/Improving-Employee-Retention-by-Predicting-Employee-Attrition-Using-Machine-Learning

This project aims to leverage machine learning techniques to predict employee attrition, allowing organizations to identify at-risk employees and implement strategies to improve retention rates.

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MayurDivate/DeepCancerSignatures

This repository contains code used to build and interpret a deep learning model. It is a DNN classifier trained using gene expression data (TCGA). Then is interpreted to identify cancer specific gene expression signatures.

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afairless/binary_classification_shap

Run histogram-based gradient boosted trees binary classifier on generated data and interpret results with standard metrics, SHAP, and supervised clustering

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yugwangyeol/2021-BigContest

[Competition] 2021 Big-Contest 정형 데이터 분석 분야 : 홍수 예방을 위한 댐 유입량 예측 모델 개발

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premstaller1/SHAP-DS2

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mbagiev/nyc-taxi-trip-duration-prediction

Prediction of NYC taxi trip duration using machine learning

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kahramankostas/IoTGeM

IoT Attack Detection with machine learning

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cee8/loan-approval-system

Proof of concept unbiased loan calculator.

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stsibikov/Modern-Data-Analytics

Курс с упором на обработку данных и Feature Engineering, обнаружение тенденций, проверку гипотез и визуализацию данных с помощью pandas и matplotlib.

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GabrielJobert/PCA_explainability_Paper---MATH60629A_MACHINE_LEARNING_I

This project aims to enhance the interpretability of Principal Component Analysis (PCA) by integrating explainability tools and advanced dimensionality reduction techniques such as UMAP or t-SNE.

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aldotestino/hmi-xai-project

This project uses machine learning to predict diabetes and provides explanations through SHAP and PCA, displayed in an intuitive user interface.

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LittleHaku/BreastCancer-ClassifierAnalysis

A Bachelor's Thesis project analyzing and comparing classifiers for breast cancer detection using fine needle aspiration biopsies. Includes Jupyter Notebooks for model training and evaluation, and a LaTeX document detailing the methodology and results. Features SHAP for explainable AI analysis.

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AnxiousCodeGeek/heartAttack-modelEvaluation

Performed model evaluation using evaluation metrics such as accuracy, precision, recall, F1-score etc. Then model interpretation using feature importance, SHAP and LIME. Finally , evaluated model robustness and stability through techniques like bootstrapping or Monte Carlo simulations.

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LittleHaku/breast-cancer-classification-web

A web app developed for my Bachelor's Thesis to compare classifiers for detecting malignant tumors from fine needle aspiration biopsies. It includes classifier metrics, SHAP analysis for feature contributions, a classifier comparison tool, and a project overview slideshow.

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ModelOriented/kernelshap

Efficient R implementation of SHAP

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GPUK79/data-science-portfolio

This repository contains the Python scripts that I have written and run to execute a series of analytic model developments using datasets taken from the book "The Elements of Statistical Elements" by Hastie, Tibshirani, Friedman

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Xinbingru/COFsMembraneML

A machine learning implementation of an interpretable model for membrane separation performance prediction of COFs materials.

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PulmonomicsLab/mcdr-mtb-standalone-v2

Multi-class classification of drug resistance in MTB clinical isolates

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carlacodes/boostmodels

gradient-boosted regression and decision tree models on behavioural animal data

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tedoaba/House-Price-Prediction-App

House-Price-Prediction-App

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jroberts225/SHAP-LIME-for-UNSW-15

Scripts for generating SHAP & LIME explainations and their corresponding plots

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hoomanbing/Twitter-Data-Sentiment-Analysis-using-Ensemble-Learning-and-XAI

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wyattowalsh/higher-education-simulation

Full Python implementation of an agent-based simulation model of generalized higher education institutions. Thousands of experiments are conducted and model feature significance is found through regression, SHAP, and permutation.

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Ashutosh27ind/census_income_prediction

The case study is a traditional supervised binary classification problem based on the UCI Machine Learning Repository "adult" dataset.

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izam-mohammed/shap Fork of shap/shap

SHAP (SHapley Additive exPlanations) is an open-source library for model interpretability and explainable AI. It provides a unified framework for interpreting and understanding the predictions of any machine learning model, including deep neural networks, gradient boosting machines and more.

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rdolor/research-collections

Contains a collection of my experimentations, explorations, and data analysis of random datasets

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Montimage/maip

Montimage AI Platform (MAIP) provides users with easy access to AI services developed by Montimage, through a friendly and intuitive interface.

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NaquibAlam/TheMisfits

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zsxkib/Most-Under-and-Over-Priced-Cars

Determine what influences and drives car prices given technical specs and identify which car(s) are the most under/overpriced and why.

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TannerGilbert/Model-Interpretation

Overview of different model interpretability libraries.

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JK-Future-GitHub/NBA_Champion

I will predict the 2023 NBA Champion using Machine Learning

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JK-Future-GitHub/NBA_MVP

I will predict the 2023 NBA MVP using Machine Learning

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SirWilliam254/Feature-Importance

This repo is all about feature importance. Whereby we look at the ways one can identify if a feature is worth having in the model or rather if it has a significant influence in the prediction. The methods are model-agnostic.

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aaronwtr/interpreting-ml-based-drops

This repository accompanies my research into the interpretability of DNA Damage Repair Outcome Predictors (DROPs). By analyzing these models using interpretability methods, we hope to uncover what features specifically are driving the accuracy of these prediction models.

Language: Python - Size: 2.89 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

ytanaka-bio/cisMultiDeep

Workflow to identify functional cis-regulatory regions for each annotated cell type

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carlacodes/neuraldecoding

scripts used for neural decoding of single and multi unit auditory cortex data

Language: Python - Size: 3.37 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 2 - Forks: 1

ksharma67/Partial-Dependent-Plots-Individual-Conditional-Expectation-Plots-With-SHAP

The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition.

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Kaushikjas10/Liquefaction-gravel-eml-2023

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.

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josedv82/NBA_Schedule_XGBoost_Classifier

Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.

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fau-masters-collected-works-cgarbin/shap-experiments-image-classification

Exploring SHAP feature attribution for image classification

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yerlikayaperi/shap-for-chem Fork of schwallergroup/CuCNCC

Complementary code to a project on the potential of machine learning as a tool for chemical synthesis using SHAP.

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venkat-a/Term-Deposit-Marketing-Prediction

client subsection to a term deposit

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ZeyTrack/digit-recognizer

identify digits from a dataset of tens of thousands of handwritten images.

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akthammomani/Menara-App-Predict-House-Price-CA

Build a Web App called Menara to Predict, Forecast House Prices and search GreatSchools in California - Bay Area

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Davide-Ettori/XAI_Research-Explainable-Neural-Networks

Research on various XAI methods: NAM, SHAP, EBM and Adversarial Attack

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LennardZuendorf/thesis-files

Collection of associated files for my bachelor thesis

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back1ply/Key-Influencers

Replicate Power BI Key Influencer visual in python

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IvanSaravanja2306/Credit_Card_Approval_Prediction

Credit Card Approval Prediction based on users' historic data.

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knaaga/death-risk-prediction-models

Tree based models to predict 10 yr risk of death using the NHANES epidemiology dataset

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REAtes/Directing-App-Customers-to-Subscription

The primary goal of this project is to convert free users of a financial tracking app into paid members. This conversion will be achieved by building a model that identifies users who are unlikely to enroll in the paid version of the app.

Language: Python - Size: 1.84 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

vigneashpandiyan/Additive-Manufacturing-Sensor-Selection-Acoustic-Emission

Sensor selection for process monitoring based on deciphering acoustic emissions from different dynamics of the Laser Powder Bed Fusion process using Empirical Mode Decompositions and Interpretable Machine Learning

Language: Python - Size: 155 KB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 1

henningheyen/ResponsibleAI-Project

This project aims to touch on the most important topics in the field of Responsible AI including bias, fairness, interpretability, error analysis and counterfactuals. Why apply those concepts to the adult dataset

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erik1110/Data-Science

iThome 13th-ironman (2021) - Data Science Learning Roadmap about Python

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annabelleluo/HELOC

This is the HKU STAT3612 project, 2020. This is an interpretable machine learning project for credit scoring with Home Equity Line of Credit data.

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ayseirmak/ModelExplainability_TurkishSentimentAnalysis

An explainability model that can be applied to BERT-based Turkish sentiment analysis models has been developed and its performance has been compared with model spesific Layer-wise relevance propogation expailanbility model of Hila Chefer.

Language: Python - Size: 2.06 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

beckypangpang/horse-racing-prediction-SHAP

This repository contains the code for machine learning models designed to predict the outcomes of horse races, with SHAP (SHapley Additive exPlanations) interpretation incorporated for enhanced model interpretability.

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w3raza/shapProjekt Fork of Saffons/shapProjekt

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etetteh/e

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Vicarious987/cub-orders-number-forecasting

Прогнозирование числа заказов такси на основе временных рядов.

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Vicarious987/car_price_prediction

Предсказание стоимости автомобилей.

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Vicarious987/hotel_false_booking_detection

Предсказание отмены брони номера в отеле.

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rishuatgithub/explainable-ai-app

Streamlit app repository for Explainable AI application

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josesousaribeiro/XAI-Benchmark

This is a repository for reproducibility purposes. In this research, a large number of datasets were used to create different ML models, which were then explained by XAI measures. Seeking to identify situations where XAI measures agreed or disagreed with each other.

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josesousaribeiro/eXirt-XAI-Pipeline

This is a repository for reproducibility purposes. In this research, a large number of datasets were used to create different ML models, which were then explained by XAI measures. Proposing a new measure of XAI called eXirt.

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McGill-MMA-EnterpriseAnalytics/datasectuals

Predicting whether or not a person deposits money after a marketing campaign. Gain insights to develop the best strategy in the next marketing campaign

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rhenkin/rforceplots

Wrapper for shapjs node package for easy force plots in R without Python dependencies

Language: R - Size: 248 KB - Last synced at: 23 days ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

ksharma67/Heart-Failure-Prediction

This problem is a typical Classification Machine Learning task. Building various classifiers by using the following Machine Learning models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Light GBM and Support Vector Machines with RBF kernel.

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