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Search results for shap
shap
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41 search results found
Shap
⭐
21,595
A game theoretic approach to explain the output of any machine learning model.
Mljar Supervised
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2,867
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Shapash
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2,547
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Explainerdashboard
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2,118
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
Shap Hypetune
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496
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
Fasttreeshap
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485
Fast SHAP value computation for interpreting tree-based models
Learning_to_rank
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184
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.
Powershap
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153
A power-full Shapley feature selection method.
Atom
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137
Automated Tool for Optimized Modelling
Timeshap
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127
TimeSHAP explains Recurrent Neural Network predictions.
Probatus
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110
Validation of classifiers and data used to develop them
Survex
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79
Explainable Machine Learning in Survival Analysis
Treeshap
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72
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Survshap
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65
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Awesome Shapley Value
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63
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Shapviz
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57
R package for SHAP plots
Shapml.jl
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50
A Julia package for interpretable machine learning with stochastic Shapley values
Xplainable
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48
Real-time explainable machine learning for business optimisation
Fooling Lime Shap
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47
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
Kernelshap
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28
Efficient R implementation of SHAP
Ai Hackathon
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18
🏆데이콘 AI해커톤 대회 우수상 솔루션🏆
Xai Tool4gee
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16
A Colab notebook for land cover mapping and monitoring using Earth Engine
Model Interpretation
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16
Overview of different model interpretability libraries.
Modeling Uncertainty Local Explainability
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16
Local explanations with uncertainty 💐!
Explainableml Vision
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14
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.
Sotai
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12
Working Women
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11
Code for the paper 'Working Women and Caste in India' (ICLR 2019 AI for Social Good Workshop)
Streamlit Shap
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10
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
Shapobjectdetection
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8
SHAP-Based Interpretable Object Detection Method for Satellite Imagery
Wavemap_paper
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7
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.
Masters Chance Of Admit
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7
A website that provides analytics on how different features contribute to your chances of getting into a university of your choice.
Aix360 Introduction
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6
Introduction to explaining data and machine learning models with aif360
Onconetexplainer
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6
OncoNetExplainer: Explainable Prediction of Cancer Types Based on Gene Expression Data
Shapley
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6
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
Tinyshap
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6
Python package providing a minimal implementation of the SHAP algorithm using the Kernel method
Cf Shap
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5
Counterfactual SHAP: a framework for counterfactual feature importance
Dynamic Shap Plots
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5
Enabling interactive plotting of the visualizations from the SHAP project.
Shapflex
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5
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
Streamlit Shap Explorer
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5
A Streamlit Web Application that predicts the genre of a song, interactively explores the corresponding SHAP values and locally explains a CatBoost Multi Classification model
Explainability_for_photonics
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5
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
Xper
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5
A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.
1-41 of 41 search results
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