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16 search results found
Shapash
⭐
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.
Low Light Image Enhancement
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239
Python implementation of two low-light image enhancement techniques via illumination map estimation
Interpretdl
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203
InterpretDL: Interpretation of Deep Learning Models,基于『飞桨』的模型可解释性算法库。
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
Gnuradio Grc Examples
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18
This repository will have all my gnuradio exsamples
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 💐!
Lime
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12
Implementation of the paper, "LIME: Low-Light Image Enhancement via Illumination Map Estimation", which is for my graduation thesis.
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.
Fine Grained Sentiment App
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9
A Flask LIME explainer app for fine-grained sentiment classification.
Awesome Lime Scooter Notifier
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9
Gets a list of closest lime scooters to a location, or notifies you if one is close
Lime Caae
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8
Pytorch Implementation of the Explainable Conditional Adversarial Autoencoder using LIME
Aix360 Introduction
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6
Introduction to explaining data and machine learning models with aif360
Visualime
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5
Implementation of LIME focused on producing user-centric local explanations for image classifiers.
Xwhy
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5
Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations
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