marcotcr/lime
Lime: Explaining the predictions of any machine learning classifier
Repository profile
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Repository updates
Get generated TeamHG-Memex/eli5 development summaries by email, or follow the weekly and monthly RSS feeds.
Sign in to subscribe by email. RSS feeds are public.
Sign in to subscribeTracked growth, recent movement, and commit velocity from stored repository snapshots.
Latest capture 2026-07-16 03:06
7 captures since 2026-05-22
Stars from baseline +1
All tracked data
Frameworks, package managers, ecosystems, and dependency manifests found during catalog scans.
Scanned 2026-07-16 03:06
requirements-test.txt
python ecosystem,
3 dependencies
requirements.txt
python ecosystem,
8 dependencies
setup.cfg
python ecosystem,
0 dependencies
setup.py
python ecosystem,
10 dependencies
docs/requirements.txt
python ecosystem,
7 dependencies
Searchable topics, generated tags, and stack labels that explain where this repository fits.
Agent instructions and tool configuration paths found in the repository tree.
AI agent config detected
Key config paths
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
scikit-learn: machine learning in Python
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.