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Search results for machine learning explainable ml
explainable-ml
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machine-learning
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67 search results found
Pytorch Grad Cam
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8,723
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Interpret
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5,992
Fit interpretable models. Explain blackbox machine learning.
Tensorwatch
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3,333
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Awesome Machine Learning Interpretability
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3,241
A curated list of awesome responsible machine learning resources.
Shapash
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2,547
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Aix360
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1,471
Interpretability and explainability of data and machine learning models
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
Imodels
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1,229
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Responsible Ai Toolbox
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1,187
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Trulens
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1,109
Evaluation and Tracking for LLM Experiments
Xai
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1,060
XAI - An eXplainability toolbox for machine learning
Omnixai
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674
OmniXAI: A Library for eXplainable AI
Aspect Based Sentiment Analysis
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413
💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
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]
Datascience_artificialintelligence_utils
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369
Examples of Data Science projects and Artificial Intelligence use-cases
Carla
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216
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Shapley
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198
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Xaience
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100
All about explainable AI, algorithmic fairness and more
Sagemaker Explaining Credit Decisions
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87
Amazon SageMaker Solution for explaining credit decisions.
Classix
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85
Fast and explainable clustering in Python
Deep_xf
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84
Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
Cade
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81
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Survex
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79
Explainable Machine Learning in Survival Analysis
Acv00
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73
ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.
Talktomodel
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68
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
Awesome Shapley Value
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63
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Cxplain
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59
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
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.
Xplainable
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48
Real-time explainable machine learning for business optimisation
Clustershapley
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40
Explaining dimensionality results using SHAP values
Mace
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29
Model Agnostic Counterfactual Explanations
Shap_fold
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28
(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining
Ilasp Releases
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26
Sirus.jl
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25
Interpretable Machine Learning via Rule Extraction
Explanation Quality Recsys
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23
Post Processing Explanations Paths in Path Reasoning Recommender Systems with Knowledge Graphs
Hyperbox Brain
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23
A scikit-learn compatible hyperbox-based machine learning library in Python
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
Diabetes_use_case
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19
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Dlime_experiments
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16
In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Lernd
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15
Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.
Teex
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14
A Toolbox for the Evaluation of machine learning Explanations
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.
Rseslib
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13
Rough set and machine learning data structures, algorithms and tools, including algorithms for discernibility matrix, reducts, decision rules, classification (RoughSet, KNN, RIONIDA, AQ15, C4.5, SVM, NeuralNetwork and many others), discretization (1R, Entropy Minimization, ChiMerge, MD), and tool for interactive and explainable machine learning.
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".
Recourse
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12
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Pytolemaic
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10
Toolbox for analysis of model's quality and model's description. For further details see
Article Information 2019
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10
Article for Special Edition of Information: Machine Learning with Python
Malnet Image
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9
A large-scale database of malicious software images
Responsible Ai Workshop
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9
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice
Atgfe
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8
Automated Transparent Genetic Feature Engineering
Jsm_2018_paper
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8
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlinepro
Transparency Guidelines
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8
Minimize the risks and maximize the benefits of using data-driven technologies within government processes, programs and services through transparency. | Réduire les risques et à maximiser les avantages liés à l’utilisation de technologies axées sur les données, dans le cadre de processus, programmes et services gouvernementaux, grâce à la transparence.
U Cam
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8
Visual Explanation using Uncertainty based Class Activation Maps
Iai Clinical Decision Rule
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8
Interpretable clinical decision rules for predicting intra-abdominal injury.
Explabox
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8
Explore/examine/explain/expose your model with the explabox!
Xai Analytics
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7
XAI-Analytics is a tool that opens the black-box of machine learning. It helps the user to understand the decision-making process of machine learning models.
Counterfactual Explanations Mdp
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7
Code for "Counterfactual Explanations in Sequential Decision Making Under Uncertainty", NeurIPS 2021
Pygol
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7
A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.
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
Slise
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5
Robust regression algorithm that can be used for explaining black box models (R implementation)
Explainable Ml Papers
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5
A list of research papers of explainable machine learning.
Pyslise
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5
Robust regression algorithm that can be used for explaining black box models (Python implementation)
Xi Method
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5
Xi method
Keras Explainable
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5
Efficient explaining AI algorithms for Keras models
Cf Shap
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5
Counterfactual SHAP: a framework for counterfactual feature importance
Distillml
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5
An R package providing functions for interpreting and distilling machine learning models
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