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12 search results found
Shapash
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2,547
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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.
Awesome Shapley Value
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63
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Lime For Time
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47
Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
Fooling Lime Shap
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47
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
Live
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35
Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN
Interpretable Ml
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17
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
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).
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.
Lime Interpretable Ml
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10
An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learning algorithm - in this case a Radom Forest regressor.
Cml_amp_churn_prediction
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9
Build an scikit-learn model to predict churn using customer telco data.
Xwhy
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5
Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations
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