shapley-additive-explanations | Topic

Topic: "shapley-additive-explanations"

AstraZeneca/awesome-shapley-value

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

Size: 622 KB - Last synced at: 16 days ago - Pushed at: almost 3 years ago - Stars: 150 - Forks: 12

gtzjh/mymodels

Assemble an efficient interpretable machine learning workflow.

Language: Python - Size: 2.62 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 22 - Forks: 3

haghish/shapley

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

Language: R - Size: 6.17 MB - Last synced at: 28 days ago - Pushed at: about 2 months ago - Stars: 16 - Forks: 1

LamineTourelab/Explainable-AI

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

Size: 8.79 KB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 7 - Forks: 0

balajissp/dash-shap-components

Language: Python - Size: 2.23 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 6 - Forks: 1

jwuphysics/gnn-linking-lengths

Measuring galaxy environmental distance scales with GNNs and explainable ML models

Language: Jupyter Notebook - Size: 11.3 MB - Last synced at: 2 months ago - Pushed at: 12 months ago - Stars: 4 - Forks: 0

anniebritton/Ecological-Drought-ML-Modeling

📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins

Language: Jupyter Notebook - Size: 5.01 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 4 - Forks: 2

carlacodes/boostmodels

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

Language: Python - Size: 1.24 GB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 3 - Forks: 0

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.

Language: Jupyter Notebook - Size: 253 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 3 - Forks: 0

upunaprosk/la-tda

Official code for EACL Workshop paper Can BERT eat RuCoLA? Topological Data Analysis to Explain

Language: Jupyter Notebook - Size: 7.36 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 3 - Forks: 0

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.

Language: Jupyter Notebook - Size: 12.9 MB - Last synced at: about 2 months ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 0

g-aditi/customer-personality-analysis

Using a Kaggle dataset, customer personality was analysed on the basis of their spending habits, income, education, and family size. K-Means, XGBoost, and SHAP Analysis were performed.

Language: Jupyter Notebook - Size: 1.68 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 1

yhe-rs/hac-rf-shap

Determining Feature Importance by Integrating Random Forest and SHAP in Python

Language: Jupyter Notebook - Size: 4.46 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 2 - Forks: 0

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.

Language: Jupyter Notebook - Size: 1.07 MB - Last synced at: 7 days ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

iamlmn/simpleML

No-code Machine learning (Pre-alpha)

Language: HTML - Size: 189 MB - Last synced at: 10 months ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

LightnessOfBeing/ImpreciseSHAP

Implementation of the algorithm described in the paper "An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data"

Language: Jupyter Notebook - Size: 101 KB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 2 - Forks: 0

DQ4781/LF-ShapAnalysis

An Analysis of Lassa Fever Outbreaks in Nigeria using Machine Learning Models and Shapley Values

Language: Jupyter Notebook - Size: 20.4 MB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

knmlprz/ShapEmotionsCorrectionFrontend 📦

Frontend for ShapEmotionsCorrectionAPI

Language: JavaScript - Size: 205 KB - Last synced at: 8 days ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

Sebastian1981/CustomerAnalytics_CreditDefaultPrediction

credit default prediction app

Language: Jupyter Notebook - Size: 3.92 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 1

Sebastian1981/Sales_Prediction

Use machine learning to find out what drives sales and predict sales

Language: Jupyter Notebook - Size: 2.22 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

arpansharma-11/XAI_breastCancer

XAI analytics to understand the working of SHAP values

Language: Jupyter Notebook - Size: 2.82 MB - Last synced at: over 1 year ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

arpansharma-11/XAI_heartDataset

XAI analytics to understand the working of SHAP values

Language: Jupyter Notebook - Size: 236 KB - Last synced at: over 1 year ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

baraki-weldat/Data-Science-Project-A1F

In this data science project, an eXplainable Hate Speech Classification model developed with BERT and SHAP Explanation tool.

Size: 1.25 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

anishjohnson/Credit_Card_Default_Prediction

In this project we predict credit card defaults using classification models.

Language: Jupyter Notebook - Size: 9.92 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

razamehar/Predicting-Bank-Customer-Churn

This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.

Language: Jupyter Notebook - Size: 9.24 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

reyhanhosavci/Shapley_Value

This script focuses on explaining a pre-trained CNN’s predictions on the MedMNIST dataset using Deep SHAP. Shapley values are computed for each pixel, summed to create a single Shapley score per image, and saved alongside labels in CSV files for interpretability in medical image classification.

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

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.

Language: Jupyter Notebook - Size: 9.32 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

Atharva309/XAI_diabetes

Explores diabetes prediction using various ML models and XAI techniques (SHAP, LIME, ALE) on the Pima Indian Diabetes dataset.

Language: Jupyter Notebook - Size: 9.37 MB - Last synced at: about 2 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 616 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

grigoryangayane/Tranportation_XAI

AI applications can be found in various real-world systems, including vehicle system design and real-time car accident prediction. There is an increasing need to better explain AI-driven processes, especially in terms of potential legal disputes that might result from AI decisions. This analysis addresses this explainability and legal issues.

Language: Jupyter Notebook - Size: 3.53 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0