{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Lime explains individual predictions from any black-box machine learning classifier by fitting local linear models around specific instances, supporting text, tabular, and image data.","skillfed_tags":["model-explainability","interpretability"],"use_cases":["Debug why a text classifier predicted a specific sentiment or category for a document.","Understand which features in tabular data drove a model's prediction for a particular row.","Visualize which regions of an image influenced a classifier's prediction (e.g., cat vs. dog).","Generate explanations for regulatory compliance when model decisions affect users.","Identify data quality issues or unexpected feature interactions by examining local approximations."],"what_it_does":"Lime is a model-agnostic explanation tool that helps you understand what machine learning classifiers are doing by generating local interpretable approximations of their behavior. It works by perturbing instances around a specific prediction, weighting samples by proximity, and fitting a sparse linear model that approximates the classifier's decision boundary locally. This reveals which features most influenced a particular prediction.\n\nThe package supports text classifiers, tabular data (numerical or categorical), and image classifiers. It requires only that your classifier outputs class probabilities; built-in support for scikit-learn is included. Explanations are generated as interactive HTML visualizations or matplotlib plots, making them suitable for notebooks and reports. The approach is based on the 2016 paper on local interpretable model-agnostic explanations.","worth_installing":"Yes, with conditions. Lime is well-established and permissively licensed, but it has been dormant since June 2020 and may have compatibility gaps with current ML frameworks. Install it if you need model-agnostic explanations for existing classifiers and can verify it works with your specific dependencies; avoid it if you require active maintenance or support for the latest framework versions."},"id":"lime","links":{"html":"https://skillfed.io/packages/lime","md":"https://skillfed.io/packages/lime.md","pypi":"https://pypi.org/project/lime/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2020-06-26","license_spdx":null,"license_treatment":"permissive","name":"lime","python_support":"supports_current","summary":"Local Interpretable Model-Agnostic Explanations for machine learning classifiers"},"popularity":{"monthly_downloads":417603,"position":6814,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.0.1"}