Introducing TensorFlow Decision Forests
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TF-DF is a collection of production-ready state-of-the-art algorithms for training, serving and interpreting decision forest models (including random forests and gradient boosted trees). You can now use these models for classification, regression and ranking tasks - with the flexibility and composability of the TensorFlow and Keras.
- Beginners will find it easier to develop and explain decision forest models. There is no need to explicitly list or pre-process input features (as decision forests can naturally handle numeric and categorical attributes), specify an architecture (for example, by trying different combinations of layers like you would in a neural network), or worry about models diverging. Once your model is trained, you can plot it directly or analyze it with easy to interpret statistics.
- Advanced users will benefit from models with very fast inference time (sub-microseconds per example in many cases). And, this library offers a great deal of composability for model experimentation and research. In particular, it is easy to combine neural networks and decision forests.