Self-explanatory tutorials for different model-agnostic and model-specific XAI methods
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Updated
May 16, 2024 - Jupyter Notebook
Self-explanatory tutorials for different model-agnostic and model-specific XAI methods
counterfactuals: An R package for Counterfactual Explanation Methods
SLISEMAP: Combining supervised dimensionality reduction with local explanations
Local Universal Rule-based Explanations
We've developed a powerful binary dog and cat image classifier, driven by advanced deep learning techniques, and enhanced its transparency using Local Interpretable Model-agnostic Explanations (LIME). Witness the magic as the model accurately predicts dog and cat images while LIME reveals the intricate decision-making process behind each result.
A machine learning project developing classification models to predict COVID-19 diagnosis in paediatric patients.
Classifier Analysis and Fairness Considerations
A Global Model-Agnostic Rule-Based XAI Method based on Parameterised Event Primitives for Time Series Classifiers
Model-independent visual explanation methods for image classifiers.
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