Learning about Interpretable Machine Learning Methods.

Decompose Thermo Gravimetrical Analysis (TGA) curves into simpler logistic curves representing mass-change events with a chemical interpretation. All of the analysis is performed with the TensorFlow library for the creation of a NN-analogous model and optimization.

Permutation feature importance

Generating counterfactual samples for machine learning model with SCM and prototypes.

Pytorch example of path-explain using Pytorch

Course project for CS726 @ IIT Bombay.

A curated list of awesome machine learning interpretability resources.

Source code for the paper "Interpretable models from distributed data via merging of decision trees" (Artur Andrzejak, Felix Langner, Silvestre Zabala, CIDM 2013).

an end to end anomaly intrusion base on deep learn

Supplementary programmes for DeRDaVa: Deletion-Robust Data Valuation for Machine Learning.

Investigating Machine Learning explainability in credit risk models by utilising LIME and DiCE methods

Enhanced CNN model for malaria cell classification, featuring Class Activation Mapping (CAM) as a non-agnstic technique for anomaly localization and LIME (Local Interpretable-agnostic Explanation) for interpretability, ensuring high accuracy and transparent AI diagnostics.

JAX-based Model Explanation and Interpretation Library

Notebooks for "Interpretable Machine Learning" course at University of Warsaw, 2021. Each homework utilises different XAI and ML techniques on different data sets.

Patient No Show Predictive Modeling Using RIPPER and Hoeffding Trees Algorithms

Paper and resources collections about interpretable AI (XAI)

Neural model interpretation on MRI data

The open-access code of an interpretable machine learning-based method for room temperature prediction in a non-domestic building.