marcotcr/lime
Lime: Explaining the predictions of any machine learning classifier
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Appears on
Quick read
Latest capture 2026-09-10 03:08
Archive status reflects the last catalog sync, not a maintenance assessment. Check recent releases, issue responses, and fit for your project with the repository shortlisting checklist.
1 path
Agent instructions and tool configuration found in this repository.
GitHub Copilot
9 observed captures since 2026-05-22. Observed captures are shown by default.
Stars from first capture +53
Observed captures only
All tracked data
Observed snapshots
Observed snapshots
Nearest indexed repositories by embedding similarity.
Lime: Explaining the predictions of any machine learning classifier
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
Fit interpretable models. Explain blackbox machine learning.
Algorithms for explaining machine learning models
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
scikit-learn: machine learning in Python