  Machine learning pipeline potentially consists of several stages of
operations like data preprocessing, feature engineering and machine learning
model training. Each operation has a set of hyper-parameters, which can become
irrelevant for the pipeline when the operation is not selected. This gives rise
to a hierarchical conditional hyper-parameter space. To optimize this mixed
continuous and discrete conditional hierarchical hyper-parameter space, we
propose an efficient pipeline search and configuration algorithm which combines
the power of Reinforcement Learning and Bayesian Optimization. Empirical
results show that our method performs favorably compared to state of the art
methods like Auto-sklearn , TPOT, Tree Parzen Window, and Random Search.
