Feature assessment and importance of Machine Learning Models using SHAP and CXPlain libraries
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Updated
May 7, 2021 - Jupyter Notebook
Feature assessment and importance of Machine Learning Models using SHAP and CXPlain libraries
Chest X-Ray Images (Pneumonia) classification: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
Repository for the Linkit Beginner Challenge on Explainable ML using SHAP values.
A method for conditional shapley value estimation, built off the shapr package: https://github.com/NorskRegnesentral/shapr/tree/master
Experiments on machine learning explainability
Code for the paper Tětková et al.: Knowledge Graphs for Empirical Concept Retrieval (accepted to The 2nd World Conference on eXplainable Artificial Intelligence).
XMLX GitHub configuration
Graduate research project in computer vision and deep learning explainability
explainable and interpretable methods for AI and data science
Streamlit App to inspect Black Box Models
Data Science Examples
Adaptation of the official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks [CVPR 2021] to ESM.
Collection of notebooks performing analysis of data and visualisations of outputs for a research paper on evaluation of GHG emissions in Burmese reservoirs
Implementing algorithms based on the analysis of the gradients in NN computational graphs to provide nice insights for Explainable AI
Explaining blackbox predictions using python libraries.
A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.
A Team Project on developing COVID-19 predictor with chest X-Ray images as dataset under Data Science with R subject of Otto-von-Guericke-University, Magdeburg
Contribution to the PatchCamelyon Challenge (semester project in image processing)
Repository containing sample datasets, models and notebooks to start using EXPAI.
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