  As data science continues to grow in popularity, there will be an increasing
need to make data science tools more scalable, flexible, and accessible. In
particular, automated machine learning (AutoML) systems seek to automate the
process of designing and optimizing machine learning pipelines. In this
chapter, we present a genetic programming-based AutoML system called TPOT that
optimizes a series of feature preprocessors and machine learning models with
the goal of maximizing classification accuracy on a supervised classification
problem. Further, we analyze a large database of pipelines that were previously
used to solve various supervised classification problems and identify 100 short
series of machine learning operations that appear the most frequently, which we
call the building blocks of machine learning pipelines. We harness these
building blocks to initialize TPOT with promising solutions, and find that this
sensible initialization method significantly improves TPOT's performance on one
benchmark at no cost of significantly degrading performance on the others.
Thus, sensible initialization with machine learning pipeline building blocks
shows promise for GP-based AutoML systems, and should be further refined in
future work.
