{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"dtreeviz renders decision trees from scikit-learn, XGBoost, LightGBM, Spark MLlib, and TensorFlow as interactive visualizations to help understand and interpret how these models make predictions.","skillfed_tags":["model-interpretation","visualization","tree-based-ml"],"use_cases":["Visualize a trained decision tree to understand which features matter most at each split.","Generate publication-quality diagrams of trees for model documentation and reports.","Debug predictions by inspecting the decision paths individual trees take.","Teach machine learning concepts by showing how decision trees partition feature space visually.","Interpret model behavior for regulatory or compliance audits by displaying exact decision logic."],"what_it_does":"dtreeviz is a Python visualization library that transforms decision trees from popular machine learning frameworks into clear, interpretable diagrams. It supports scikit-learn, XGBoost, LightGBM, Spark MLlib, and TensorFlow, making it a bridge between model training and human understanding. The library renders trees as visual graphs that show how features split at each node and how predictions flow through the model, inspired by educational design principles.\n\nThe package depends on graphviz for rendering, pandas and numpy for data handling, scikit-learn for tree structure access, matplotlib for graphics, and colour for visual styling. It's primarily used by data scientists and ML engineers who need to explain model behavior to stakeholders, debug model decisions, or learn how tree-based models work internally.","worth_installing":"Yes, if you work with tree-based models and need to visualize or interpret them. The library is stable, permissively licensed, and has low install friction. The aging maintenance status (no release in 224 days) is a minor concern if you rely on cutting-edge framework versions, but core functionality remains sound for current releases."},"id":"dtreeviz","links":{"html":"https://skillfed.io/packages/dtreeviz","md":"https://skillfed.io/packages/dtreeviz.md","pypi":"https://pypi.org/project/dtreeviz/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2026-01-02","license_spdx":null,"license_treatment":"permissive","name":"dtreeviz","python_support":"supports_current","summary":"A Python 3 library for sci-kit learn, XGBoost, LightGBM, Spark, and TensorFlow decision tree visualization"},"popularity":{"monthly_downloads":84225,"position":14014,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.3.2"}