FeatureMAP (Feature-preserving Manifold Approximation and Projection) is an interpratable dimensionality reduction tool.
-
Updated
Jun 3, 2024 - Jupyter Notebook
FeatureMAP (Feature-preserving Manifold Approximation and Projection) is an interpratable dimensionality reduction tool.
PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
[CVPR 2024] CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Fit interpretable models. Explain blackbox machine learning.
Implementation of LIME focused on producing user-centric local explanations for image classifiers.
Efficient R implementation of SHAP
A curated list of awesome responsible machine learning resources.
Local interpretability for survival models
Self-Supervised Adaptive and Interpretable Anomaly Detection with Dynamic Operating Limits
A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis
moDel Agnostic Language for Exploration and eXplanation
[HELP REQUESTED] Generalized Additive Models in Python
This is a Python library that implements a Multi-objective Symbolic Regression algorithm. It can be used as a Machine Learning algorithm to create predictive models in the form of mathematical expressions.
Interpretable Multi Agent Reinforcement Learning with a Quality DIversity Approach
Official Implementation of the paper guided attention for interpretable motion captioning
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
counterfactuals: An R package for Counterfactual Explanation Methods
Robust regression algorithm that can be used for explaining black box models (Python implementation)
AutoML system for building trustworthy peptide bioactivity predictors
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."