{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"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/11"},{"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":"Trains and deploys decision forest models (Random Forests, Gradient Boosted Trees) within TensorFlow for classification, regression, and ranking tasks.","skillfed_tags":["decision-forests","ensemble-learning","tensorflow-integration"],"use_cases":["Train Random Forest or Gradient Boosted Tree models on tabular data using TensorFlow's Keras API and pandas DataFrames.","Build classification or regression pipelines that combine decision forests with TensorFlow's preprocessing and deployment infrastructure.","Export trained models as TensorFlow SavedModels for serving in production environments or converting to Yggdrasil format.","Interpret model predictions through decision forest-specific analysis tools available in the library.","Migrate or interoperate between TensorFlow Decision Forests and Yggdrasil Decision Forests across different platforms."],"what_it_does":"TensorFlow Decision Forests is a library for training and deploying decision forest models\u2014Random Forests, Gradient Boosted Trees, and similar ensemble methods\u2014directly within TensorFlow. It handles classification, regression, and ranking problems, and is powered by Yggdrasil Decision Forests, a C++ backend that also supports JavaScript, CLI, and Go. Models trained in TensorFlow Decision Forests can be exported as TensorFlow SavedModels and are compatible with Yggdrasil models.\n\nThe library integrates with TensorFlow's Keras API, accepting pandas DataFrames or TensorFlow datasets as input. It provides model inspection, evaluation, and export capabilities. It runs on Linux and macOS (Windows via WSL). The package depends on TensorFlow, pandas, numpy, and several other utilities, making it suitable for teams already working within the TensorFlow ecosystem who want tree-based models alongside neural networks.","worth_installing":"Yes, if you are already using TensorFlow and need tree-based models. The library is actively maintained, has no known vulnerabilities, and integrates cleanly with TensorFlow's ecosystem. Medium install friction is acceptable for a compiled package. Not recommended for Windows users without WSL, or for projects not already committed to TensorFlow."},"id":"tensorflow-decision-forests","links":{"html":"https://skillfed.io/packages/tensorflow-decision-forests","md":"https://skillfed.io/packages/tensorflow-decision-forests.md","pypi":"https://pypi.org/project/tensorflow-decision-forests/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-03-13","license_spdx":null,"license_treatment":"permissive","name":"tensorflow-decision-forests","python_support":"supports_current","summary":"Collection of training and inference decision forest algorithms."},"popularity":{"monthly_downloads":555172,"position":6028,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.12.0"}