  We introduce an automatic machine learning (AutoML) modeling architecture
called Autostacker, which combines an innovative hierarchical stacking
architecture and an Evolutionary Algorithm (EA) to perform efficient parameter
search. Neither prior domain knowledge about the data nor feature preprocessing
is needed. Using EA, Autostacker quickly evolves candidate pipelines with high
predictive accuracy. These pipelines can be used as is or as a starting point
for human experts to build on. Autostacker finds innovative combinations and
structures of machine learning models, rather than selecting a single model and
optimizing its hyperparameters. Compared with other AutoML systems on fifteen
datasets, Autostacker achieves state-of-art or competitive performance both in
terms of test accuracy and time cost.
