TensorFlow Decision Forests
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
What is TensorFlow Decision Forests?
TensorFlow Decision Forests is a library for training, serving, and interpreting decision forest models within TensorFlow. It provides state-of-the-art algorithms like Random Forests and Gradient Boosted Trees for classification, regression, and ranking tasks, seamlessly integrating with the TensorFlow and Keras ecosystems.
Machine learning engineers and data scientists who use TensorFlow and want to incorporate powerful, interpretable tree-based models into their workflows without leaving the TensorFlow environment.
Developers choose TF-DF because it offers the performance and interpretability of decision forests with the convenience of TensorFlow's API, enabling unified model development and deployment alongside neural networks.
Overview
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Use Cases
Best For
- Training interpretable machine learning models in TensorFlow
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