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Search results for interpretable machine learning
interpretable-machine-learning
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113 search results found
Interpret
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5,992
Fit interpretable models. Explain blackbox machine learning.
Awesome Machine Learning Interpretability
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3,241
A curated list of awesome responsible machine learning resources.
Dice
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1,257
Generate Diverse Counterfactual Explanations for any machine learning model.
Dalex
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1,242
moDel Agnostic Language for Exploration and eXplanation
Awesome Interpretable Machine Learning
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856
Piml Toolbox
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797
PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
Pygam
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784
[HELP REQUESTED] Generalized Additive Models in Python
Omnixai
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674
OmniXAI: A Library for eXplainable AI
Xai_resources
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634
Interesting resources related to XAI (Explainable Artificial Intelligence)
Interpretable_machine_learning_with_python
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629
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Mli Resources
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405
H2O.ai Machine Learning Interpretability Resources
Explainx
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375
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @
[email protected]
Pyss3
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307
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
Modelstudio
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305
📍 Interactive Studio for Explanatory Model Analysis
Adversarial Explainable Ai
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235
💡 Adversarial attacks on explanations and how to defend them
Cnn Exposed
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157
🕵️♂️ Interpreting Convolutional Neural Network (CNN) Results.
Etsformer
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131
PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
Gsat
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111
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
Xaience
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100
All about explainable AI, algorithmic fairness and more
Conceptbottleneck
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99
Concept Bottleneck Models, ICML 2020
Fastshap
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96
Fast approximate Shapley values in R
Tsinterpret
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83
An Open-Source Library for the interpretability of time series classifiers
Rrl
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82
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
Zennit Crp
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80
An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization
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
Talktomodel
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68
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
Machine Learning
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68
Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.
Ml Fairness Framework
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68
FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
Machine Learning For High Risk Applications Book
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60
Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
Iba
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58
Information Bottlenecks for Attribution
Convolutional Tsetlin Machine Tutorial
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51
Tutorial on the Convolutional Tsetlin Machine
Layerwise Relevance Propagation
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51
Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
Shapml.jl
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50
A Julia package for interpretable machine learning with stochastic Shapley values
Xai Iml Sota
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50
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Xaiaterum2020
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49
Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
Prototree
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46
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
Awesome Xai Evaluation
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45
Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems
Potato
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44
XAI based human-in-the-loop framework for automatic rule-learning.
Shapleyexplanationnetworks
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36
Implementation of the paper "Shapley Explanation Networks"
Awesome Neural Trees
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35
Introduction, selected papers and possible corresponding codes in our review paper "A Survey of Neural Trees"
Pytsetlinmachineparallel
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35
Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.
Benchmarking And Mli Experiments On The Adult Dataset
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32
Contains benchmarking and interpretability experiments on the Adult dataset using several libraries
Mace
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29
Model Agnostic Counterfactual Explanations
Kernelshap
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28
Efficient R implementation of SHAP
Compboost
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27
C++ implementation and R API for componentwise boosting
Cnn_visualizations
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24
Visualization of Adversarial Examples
Daam I2i
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22
Diffusion attentive attribution maps for interpreting Stable Diffusion for image-to-image attention.
Flashlight
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20
Machine learning explanations
Contrxt
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19
a tool for comparing the predictions of any text classifiers
Artemis
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19
A Python package with explanation methods for extraction of feature interactions from predictive models
Blase
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19
Interpretable Machine Learning for astronomical spectroscopy in PyTorch and JAX
Diabetes_use_case
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19
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Plaquebox Paper
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18
Repo for Tang et al, bioRxiv 454793 (2018)
Hint
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17
[CVPR 2022] HINT: Hierarchical Neuron Concept Explainer
Interpretable Ml
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17
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Survlimepy
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16
Local interpretability for survival models
Xai
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16
Genetic programming method for explaining complex black-box models
Xai Scholar
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16
Cross-field empirical trends analysis of XAI literature
Lri
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15
[ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.
Pipnet
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15
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
Netlens
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14
A toolkit for interpreting and analyzing neural networks (vision)
Lucidmode
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14
A Lucid Framework for Transparent and Interpretable Machine Learning Models.
Merlin
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14
MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.
Post Attribution Baselines
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13
The repository for the submission "Visualizing the Impact of Feature Attribution Baselines"
Vivo
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13
Variable importance via oscillations
Bcf Iv
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12
Package for heterogeneous causal effects in the presence of imperfect compliance (e.g., instrumental variables, fuzzy regression discontinuity designs)
Hc_ml
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12
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Mllp
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12
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Awesome Time Series Explainability
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11
A list of (post-hoc) XAI for time series
Causalglm
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11
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
2021l Wb Xai 1
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11
Case Study course for DS studies in Summer 2020/2021
Counterfactuals
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11
counterfactuals: An R package for Counterfactual Explanation Methods
Article Information 2019
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10
Article for Special Edition of Information: Machine Learning with Python
Pi Prl
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10
ICLR'22 Programmatic Reinforcement Learning
Label Free Xai
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10
This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.
Learning Scaffold
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9
This is the official implementation for the paper "Learning to Scaffold: Optimizing Model Explanations for Teaching"
Autopeptideml
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9
AutoML system for building trustworthy peptide bioactivity predictors
Transparentml
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9
An Introduction to Transparent Machine Learning
Ml Flask Api
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9
A simple template of a Python API (web-service) for real-time Machine Learning predictions, using scikitlearn-like models, Flask and Docker.
Cinday Rug Iml 2018
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9
Slides and other material for Cincinnati-Dayton useR presentation on interpretable machine learning with R
Class_selectivity_index
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9
On the importance of single directions for generalization(Morcos et al, ICLR 2018)
Interpretable Image Classification
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9
Interpretability methods applied on image classifiers trained on MNIST and CIFAR10
Quantified Sleep
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9
Quantified Sleep: Machine learning techniques for observational n-of-1 studies.
Hydro Interpretive Dl
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9
Interpretive deep learning for identifying flooding mechanisms
Explabox
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8
Explore/examine/explain/expose your model with the explabox!
Jsm_2018_paper
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8
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlinepro
Rnagps
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7
Interpretable model of sub-cellular RNA localization.
Gcfexplainer
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6
Global Counterfactual Explainer for Graph Neural Networks
Explaining_predictions_with_lime
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6
Shapley Values H2o Example
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6
Shapley Values with H2O AutoML Example (ML Interpretability)
Deepcoda
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6
Deep learning for personalized interpretability for compositional health data
Interpretablesdmwithjulia
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6
Slides for the "Interpretable SDM with Julia" workshop
Multiobjective_symbolic_regression
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6
This is a Python library that implements a Multi-objective Symbolic Regression algorithm. It can be used as a Machine Learning algorithm to create predictive models in the form of mathematical expressions.
Shapley_valuation
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6
PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]
Robust Counterfactuals
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6
Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"
Rulecosi
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5
RuleCOSI is a machine learning package that combine and simplifies tree ensembles and generates a single rule-based classifier that is smaller and simpler.
Xi Method
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5
Xi method
Dynamic Shap Plots
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5
Enabling interactive plotting of the visualizations from the SHAP project.
Visualime
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5
Implementation of LIME focused on producing user-centric local explanations for image classifiers.
1-100 of 113 search results
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