Topic: shapley-additive-explanations Goto Github
Some thing interesting about shapley-additive-explanations
Some thing interesting about shapley-additive-explanations
shapley-additive-explanations,XGB - SHAP XAI
User: akash-07dl
shapley-additive-explanations,In this project we predict credit card defaults using classification models.
User: anishjohnson
shapley-additive-explanations,📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins
User: anniebritton
shapley-additive-explanations,XAI analytics to understand the working of SHAP values
User: arpansharma-11
shapley-additive-explanations,XAI analytics to understand the working of SHAP values
User: arpansharma-11
shapley-additive-explanations,Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Organization: astrazeneca
shapley-additive-explanations,
User: balajissp
shapley-additive-explanations,gradient-boosted regression and decision tree models on behavioural animal data
User: carlacodes
shapley-additive-explanations,Getting explanations for predictions made by black box models.
User: datatrigger
Home Page: https://blog.vlgdata.io/post/interpretable_machine_learning_shap/
shapley-additive-explanations,An Analysis of Lassa Fever Outbreaks in Nigeria using Machine Learning Models and Shapley Values
User: dq4781
shapley-additive-explanations,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.
User: g-aditi
shapley-additive-explanations,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.
User: grigoryangayane
Home Page: http://research.nii.ac.jp/~ksatoh/jurisin2023/jurisin2023_proceedings.pdf
shapley-additive-explanations,Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
User: haghish
shapley-additive-explanations,ML implementations in Multi-scale model for lignin biosynthesis in Populus Trichocarpa
User: himasai97
shapley-additive-explanations,No-code Machine learning (Pre-alpha)
User: iamlmn
Home Page: https://iamlmn.github.io/simpleML/
shapley-additive-explanations,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.
User: josedv82
shapley-additive-explanations,Measuring galaxy environmental distance scales with GNNs and explainable ML models
User: jwuphysics
Home Page: https://arxiv.org/abs/2402.07995
shapley-additive-explanations,This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.
User: kaushikjas10
Home Page: https://doi.org/10.1016/j.compgeo.2023.106051
shapley-additive-explanations,This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
User: kaushikjas10
Home Page: https://doi.org/10.1016/j.soildyn.2022.107662
shapley-additive-explanations,Coding challenge for a job interview examining the predictors of vehicle accident severity using GB Road Safety Data
User: kingjosephm
shapley-additive-explanations,Frontend for ShapEmotionsCorrectionAPI
Organization: knmlprz
shapley-additive-explanations,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.
User: ksharma67
shapley-additive-explanations,In this repository you will fine explainability of machine learning models.
User: laminetourelab
shapley-additive-explanations,Implementation of the algorithm described in the paper "An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data"
User: lightnessofbeing
shapley-additive-explanations,Review of Hassan Sozen (1997) Priority Index for Rapid Assessment of Earthquake Vulnerability in Low Rise RC Structures.
User: macillas
shapley-additive-explanations,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.
User: razamehar
shapley-additive-explanations,Android malware detection using machine learning.
User: sachin17git
shapley-additive-explanations,Understanding the limitations of Gassmann's fluid substitution model using explainable ML
User: savinims
shapley-additive-explanations,credit default prediction app
User: sebastian1981
shapley-additive-explanations,Use machine learning to find out what drives sales and predict sales
User: sebastian1981
shapley-additive-explanations,👨💻 This repository shows how machine learning and SHAP can be leveraged to understand the reasons of production downtime ⌛
User: skswar
shapley-additive-explanations,Predict probability of default on credit
User: tysonjohn015
shapley-additive-explanations,Code for EACL Workshop paper Can BERT eat RuCoLA? Topological Data Analysis to Explain
User: upunaprosk
Home Page: https://aclanthology.org/2023.bsnlp-1.15
shapley-additive-explanations,
User: witrioktafiani
shapley-additive-explanations,Determining Feature Importance by Integrating Random Forest and SHAP in Python
User: yhe-rs
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