Showing posts from November, 2020

Adding common sense to machine learning with TensorFlow Lattice

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. linear regression, trees) can be too rigid in their functional forms. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. This blog post motivates this problem more fully, and discusses monotonic splines and lattices as a solution. While the discussion is about methods and applications, the blog also contains pointers to research papers and to the TensorFlow Lattice package that provides an implementation of these solutions.  Authors of this post are part of the team at Google that builds TensorFlow Lattice. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. For example, consider the following simple example — fitting a two-dimensional function to predict if someone wi