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Search results for machine learning explainability
explainability
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machine-learning
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51 search results found
Shap
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21,595
A game theoretic approach to explain the output of any machine learning model.
Awesome Production Machine Learning
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15,804
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Interpret
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5,992
Fit interpretable models. Explain blackbox machine learning.
Shapash
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2,547
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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.
Xai
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1,060
XAI - An eXplainability toolbox for machine learning
Flashtorch
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712
Visualization toolkit for neural networks in PyTorch! Demo -->
Awesome Graph Explainability Papers
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486
Papers about explainability of GNNs
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]
Carla
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216
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Sage
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205
For calculating global feature importance using Shapley values.
Whitebox
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184
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Zennit
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151
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Hierarchical Dnn Interpretations
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119
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
Sagemaker Explaining Credit Decisions
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87
Amazon SageMaker Solution for explaining credit decisions.
Pytorch_explain
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80
PyTorch Explain: Logic Explained Networks in Python.
Deep Explanation Penalization
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74
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
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.
Treeshap
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72
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Contextual Ai
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72
Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.
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)
Fat Forensics
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63
Modular Python Toolbox for Fairness, Accountability and Transparency Forensics
Imodelsx
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60
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Adaptive Wavelets
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60
Adaptive, interpretable wavelets across domains (NeurIPS 2021)
Cxplain
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59
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
Azimuth
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50
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
Bunkatopics
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43
🗺️ Data Cleaning and Textual Data Visualization 🗺️
Augmented Interpretable Models
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35
Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
Harmonization
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34
👋 Aligning Human & Machine Vision using explainability
Iprompt
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33
Finding semantically meaningful and accurate prompts.
Te2rules
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25
Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
Removal Explanations
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24
A lightweight implementation of removal-based explanations for ML models.
Explanation Quality Recsys
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23
Post Processing Explanations Paths in Path Reasoning Recommender Systems with Knowledge Graphs
Artemis
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19
A Python package with explanation methods for extraction of feature interactions from predictive models
Tradernet Crv2
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18
TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets
Modeling Uncertainty Local Explainability
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16
Local explanations with uncertainty 💐!
Automlx
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16
This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.
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
Diffi
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14
Interpretation of Isolation Forests
Sotai
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12
Mllp
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12
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Streamlit Shap
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10
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
Pytolemaic
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10
Toolbox for analysis of model's quality and model's description. For further details see
Cml_amp_churn_prediction
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9
Build an scikit-learn model to predict churn using customer telco data.
Responsible Ai Workshop
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9
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice
Protopdebug
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9
Implementation of Concept-level Debugging of Part-Prototype Networks
Bhad
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9
A Python library for Bayesian Anomaly Detection
Deepinfoflow
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7
Aaanalysis
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7
Python framework for interpretable protein prediction
Javaanchoradapters
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6
Getting the Anchors Explainer to work in Different Settings
Xwhy
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5
Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations
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.
Explainable Ml Papers
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5
A list of research papers of explainable machine learning.
Cf Shap
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5
Counterfactual SHAP: a framework for counterfactual feature importance
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