Yggdrasil Decision Forests logo

Yggdrasil Decision Forests (YDF) is a production-grade collection of algorithms for the training, serving, and interpretation of decision forest models. YDF is open-source and is available in C++, command-line interface (CLI), TensorFlow (under the name TensorFlow Decision Forests ; TF-DF), JavaScript (inference only), and Go (inference only). YDF is supported on Linux, Windows, macOS, Raspberry Pi, and Arduino (experimental).



  • Random Forest, Gradient Boosted Trees, CART, and variations such as Dart, Extremely randomized trees.

  • Classification, regression, ranking and uplifting.

  • Model evaluation e.g. accuracy, auc, roc, auuc, pr-auc, confidence boundaries, ndgc.

  • Model analysis e.g. pdp, cep, variable importance, model plotting, structure analysis.

  • Native support for numerical, categorical, boolean, categorical-set (e.g. text) features.

  • Native support for missing values.

  • State of the art tree learning features e.g. oblique split, honest tree, hessian score, global tree optimization.

  • Distributed training.

  • Automatic hyper-parameter tuning.

  • Fast model inference e.g. vpred, quick-scorer extended.

  • Cross compatible API and models: C++, CLI, Go, JavaScript and Python.

See the feature list for more details.

About TensorFlow Decision Forests#

TensorFlow Decision Forests is a library for training, evaluating, interpreting, and inferring decision forest models in TensorFlow. TensorFlow Decision Forests uses Yggdrasil Decision Forests for model training. TensorFlow Decision Forests models are compatible with Yggdrasil Decision Forests.

TensorFlow Decision Forests logo



TF-DF / Python API