{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/12"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"YDF trains, evaluates, and interprets decision forest models (Random Forests, Gradient Boosted Trees, and others) through a Python wrapper around the C++ Yggdrasil Decision Forests library.","skillfed_tags":["decision-forests","gradient-boosting","tabular-ml"],"use_cases":["Train Gradient Boosted Trees or Random Forest classifiers for tabular data classification tasks.","Evaluate and compare decision forest models on test datasets to measure performance.","Export trained models for deployment or import pre-trained models for inference.","Inspect and interpret decision forest models to understand feature importance and decision paths.","Build ranking or uplift models using decision forest learners for specialized prediction tasks."],"what_it_does":"YDF is a Python library for building and working with decision forest models\u2014Random Forests, Gradient Boosted Trees, and related ensemble methods. It wraps the C++ Yggdrasil Decision Forests engine to provide fast training, evaluation, and model inspection. The library is designed as the successor to TensorFlow Decision Forests and emphasizes performance and feature completeness for new projects.\n\nTypical workflows involve loading data, instantiating a learner (e.g., GradientBoostedTreesLearner), training on a dataset, evaluating on test data, and saving or loading models. The package depends on numpy, absl_py, and protobuf for numerical computation and serialization. It supports Python 3.9 through 3.13 and is actively maintained.","worth_installing":"Yes, if you need to train or deploy decision forest models in Python and work with tabular data. The active maintenance, permissive Apache 2.0 license, and lack of known vulnerabilities make it a solid choice. Install friction is moderate due to compiled wheels, but prebuilt binaries cover common platforms and Python versions (3.9\u20133.13). Not suitable if you require platforms outside macOS ARM64 or Linux x86_64."},"id":"ydf","links":{"html":"https://skillfed.io/packages/ydf","md":"https://skillfed.io/packages/ydf.md","pypi":"https://pypi.org/project/ydf/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-26","license_spdx":null,"license_treatment":"permissive","name":"ydf","python_support":"supports_current","summary":"YDF (short for Yggdrasil Decision Forests) is a library for training, serving, evaluating and analyzing decision forest models such as Random Forest and Gradient Boosted Trees."},"popularity":{"monthly_downloads":439802,"position":6651,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.16.1"}