What’s the difference between tensorflow decision forest models and sklearn decision tree models?
TensorFlow Decision Forests (TF-DF), and its younger sibling project YDF, are widely used in production. The first differences between TF-DF/YDF and SKLearn are in the available features to go from a development model to a production model while minimizing costs and mistakes. For example, TF-DF/YDF offers automated early stopping configuration, the ability to serve and combine with TensorFlow models with TensorFlow Serving and Vertex AI, fast C++ inference, distributed training support, and many ways to look / check / analyze the model.
In addition to production features, TF-DF/YDF supports advanced decision forest capabilities that are not available in SKLearn. For instance, TF-DF/YDF handle natively categorical and categorical-set features, can learn oblique splits, and support ranking and uplifting in addition to classification and regression.
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