Topic: shap Goto Github
Some thing interesting about shap
Some thing interesting about shap
shap,How to Interpret SHAP Analyses: A Non-Technical Guide
User: aidancooper
Home Page: https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/
shap,How to use SHAP values for better cluster analysis
User: aidancooper
Home Page: https://www.aidancooper.co.uk/supervised-clustering-shap-values/
shap,Build a Web App called Menara to Predict, Forecast House Prices and search GreatSchools in California - Bay Area
User: akthammomani
shap,
User: alexcoca
shap,Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Organization: astrazeneca
shap,A website that provides analytics on how different features contribute to your chances of getting into a university of your choice.
User: bbloggsbott
Home Page: https://masters-chance-of-admit.onrender.com/
shap,A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
User: cerlymarco
shap,Code for the paper 'Working Women and Caste in India' (ICLR 2019 AI for Social Good Workshop)
User: chaitjo
Home Page: https://arxiv.org/abs/1905.03092
shap,🏆데이콘 AI해커톤 대회 우수상 솔루션🏆
User: ds-wook
shap,Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
User: dylan-slack
shap,Local explanations with uncertainty 💐!
User: dylan-slack
shap,A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation
User: enbinyang
shap,Comparing 5 different XAI techniques (LIME, PermSHAP, KernelSHAP, DiCE, CEM) through quantitative metrics. Published at EDM 2022.
Organization: epfl-ml4ed
Home Page: https://arxiv.org/pdf/2207.00551.pdf
shap,This repo allows for the complete reproduction, from processed data, of all the main and supplemental figures in the manuscript Non-linear Dimensionality Reduction on Extracellular Waveforms Reveals Physiological, Functional, and Laminar Diversity in Premotor Cortex.
User: erickenjilee
shap,iThome 13th-ironman (2021) - Data Science Learning Roadmap about Python
User: erik1110
shap,TimeSHAP explains Recurrent Neural Network predictions.
Organization: feedzai
shap,This repository contains an example of how to implement the shap library to interpret a machine learning model.
User: fernandolpz
shap,A Colab notebook for land cover mapping and monitoring using Earth Engine
Organization: geoair-lab
shap,A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.
Organization: hi-paris
Home Page: https://hi-paris.github.io/XPER/
shap,SHAP-Based Interpretable Object Detection Method for Satellite Imagery
User: hiroki-kawauchi
shap,Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.
Organization: ing-bank
Home Page: https://ing-bank.github.io/probatus
shap,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.
User: jiangnanboy
shap,I will predict the 2023 NBA Champion using Machine Learning
User: jk-future-github
shap,Counterfactual SHAP: a framework for counterfactual feature importance
Organization: jpmorganchase
shap,Fast SHAP value computation for interpreting tree-based models
Organization: linkedin
shap,🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Organization: maif
Home Page: https://maif.github.io/shapash/
shap,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.
User: marvinbuss
shap,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.
User: mayurdivate
shap,SurvSHAP(t): Time-dependent explanations of machine learning survival models
Organization: mi2datalab
Home Page: https://doi.org/10.1016/j.knosys.2022.110234
shap,Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Organization: mljar
Home Page: https://mljar.com
shap,Efficient R implementation of SHAP
Organization: modeloriented
Home Page: https://modeloriented.github.io/kernelshap/
shap,R package for SHAP plots
Organization: modeloriented
Home Page: https://modeloriented.github.io/shapviz/
shap,Explainable Machine Learning in Survival Analysis
Organization: modeloriented
Home Page: https://modeloriented.github.io/survex
shap,Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Organization: modeloriented
Home Page: https://modeloriented.github.io/treeshap/
shap,An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
User: nredell
shap,A Julia package for interpretable machine learning with stochastic Shapley values
User: nredell
Home Page: https://nredell.github.io/ShapML.jl/dev/
shap,Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
User: oegedijk
Home Page: http://explainerdashboard.readthedocs.io
shap,Enabling interactive plotting of the visualizations from the SHAP project.
User: prashantsaikia
shap,A power-full Shapley feature selection method.
Organization: predict-idlab
shap,Here, we use Deep SHAP (or SHAP) to explain the behavior of nanophotonic structures learned by a convolutional neural network (CNN). Reference: https://pubs.acs.org/doi/full/10.1021/acsphotonics.0c01067
Organization: raman-lab-ucla
shap,Keras 101: A simple Neural Network for House Pricing regression
User: rodrigobressan
shap,A game theoretic approach to explain the output of any machine learning model.
Organization: shap
Home Page: https://shap.readthedocs.io
shap,streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
User: snehankekre
Home Page: https://pypi.org/project/streamlit-shap/
shap,Predicting the severity of accident
User: sonnguyen129
Home Page: https://traffic-severity-prediction.herokuapp.com/
shap,Overview of different model interpretability libraries.
User: tannergilbert
Home Page: https://gilberttanner.com/tag/model-interpretation/
shap,Interpretable machine learning based on Shapley values
User: tsurubee
shap,Automated Tool for Optimized Modelling
User: tvdboom
Home Page: https://tvdboom.github.io/ATOM/
shap,Interpretable Machine Learning for COVID-19
User: wuhanstudio
Home Page: https://arxiv.org/abs/2010.02006
shap,Real-time explainable machine learning for business optimisation
Organization: xplainable
Home Page: https://www.xplainable.io
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