{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Decomposes scikit-learn decision tree and random forest predictions into bias and per-feature contribution components, enabling interpretation of how each feature drives individual predictions.","skillfed_tags":["model-interpretability"],"use_cases":["Explain individual tree predictions to stakeholders by showing feature contributions.","Debug model behavior by decomposing predictions and identifying which features dominate decisions.","Audit model fairness by examining whether sensitive features have outsized contributions.","Validate model logic by checking that prediction = bias + sum(contributions) holds across data."],"what_it_does":"TreeInterpreter is a small library that breaks down predictions from tree-based models into interpretable components. For each prediction, it computes a bias term and a contribution value for each input feature, so you can see exactly how much each feature pushed the prediction up or down. This is useful when you need to explain individual predictions or debug why a model made a particular decision.\n\nThe package has no runtime dependencies and installs as a pure Python wheel. It is no longer maintained, with the last release in 2021-01-10 and no activity since 2023-07-18, so it may not work with newer versions without modification.","worth_installing":"Yes, if you are working with tree models and need local prediction explanations on a stable codebase. The package is simple, dependency-free, and has no known vulnerabilities. However, do not use it if you depend on active maintenance or need compatibility with very recent versions\u2014test thoroughly before deploying to production, as the project is abandoned."},"id":"treeinterpreter","links":{"html":"https://skillfed.io/packages/treeinterpreter","md":"https://skillfed.io/packages/treeinterpreter.md","pypi":"https://pypi.org/project/treeinterpreter/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2021-01-10","license_spdx":null,"license_treatment":"permissive","name":"treeinterpreter","python_support":"unspecified","summary":"Package for interpreting scikit-learn's decision tree and random forest predictions."},"popularity":{"monthly_downloads":103532,"position":12801,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.3"}