Learning about Interpretable Machine Learning Methods.
-
Updated
Mar 13, 2021 - Jupyter Notebook
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
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
A CT-scan of your CNN
A python library to agnostically explain multi-label black-box classifiers (tabular data)
B.Tech Project
How to enhance the interpretability of powerful black-box models?
Explainable Data Decompositions with Disc (AAAI 2020)
Neural Additive Models - Visualization Tool in PyTorch/Plotly-Dash
Code for paper "Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement", Neurips 2023
Add a description, image, and links to the interpretable-machine-learning topic page so that developers can more easily learn about it.
To associate your repository with the interpretable-machine-learning topic, visit your repo's landing page and select "manage topics."