#Interpretable-ml
Showing 13 of 13 repositories tagged #interpretable-ml, ranked by stars
Fit interpretable models. Explain blackbox machine learning.
Model interpretability and understanding for PyTorch
A curated list of awesome responsible machine learning resources.
High-Performance Symbolic Regression in Python and Julia
Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
Distributed High-Performance Symbolic Regression in Julia
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
H2O.ai Machine Learning Interpretability Resources
A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
A Streamlit dashboard that measures skill adaptation debt instead of predicting outcomes. It decomposes pressure into churn, novelty, and breadth to explain which roles/industries are becoming harder to staff. Includes role/industry reports, skill pressure maps, what-if scenario simulation, and a dataset explorer.