Like all machine learning algorithms, Decision Forests have hyper-parameters. The default values of those parameters give good results, but, if you really want the best possible results for your model, you need to "tune" those parameters. TF-DF makes it easy to tune parameters. Starting with TF-DF 1.0, you can use the pre-configured hyper-parameter tuning search space. Check the hyper-parameter tuning tutorial for more details.

We are happy to open source TensorFlow Decision Forests (TF-DF). 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. Decision forests are a family of machine learning algorithms with quality and speed competitive with (and often favorable to) neural networks, especially when you're working with tabular data. They're built from many decision trees, which makes them easy to use and understand - and you can take advantage of a plethora of interpretability tools and techniques that already exist today.