marcotcr/lime

Health v2 · maintenance only

Adoption not part of the score

12163 stars · 1847 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Lime (Local Interpretable Model-agnostic Explanations) is a Python library that explains the predictions of any machine learning classifier. It generates local explanations for individual predictions made by black-box models acting on text, tabular data, or images.

Use cases

• explain predictions of a machine learning classifier
• interpret black box model predictions
• understand which features influenced a text classifier prediction
• visualize feature importance for tabular data models
• explain image classifier predictions
• generate local explanations for scikit-learn models
• debug machine learning model behavior on individual predictions

When to choose

• you need model-agnostic explanations that work with any classifier exposing a predict-probability interface
• you want to explain individual predictions rather than global model behavior
• you work with text, tabular, or image classifiers and need interpretable output
• you use scikit-learn and want built-in support without extra wiring
• you want explanations embeddable in Jupyter notebooks

When to avoid

• you need global model explanations or full surrogate models rather than per-prediction explanations
• you need explanations for deep learning models with tight GPU integration where newer tools like SHAP or integrated gradients fit better
• you require active development and frequent updates, since the project is in maintenance mode
• you need sub-second explanation latency for production inference pipelines, as LIME sampling adds overhead

Facets

library · maturity maintenance

machine-learning data-visualization nlp image-processing machine-learning data-science artificial-intelligence python cross-platform explainable-ai model-interpretability local-explanations black-box-models feature-attribution scikit-learn jupyter-notebooks xai explainability interpretability

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