  Classical machine learning models, such as linear models and tree-based
models, are widely used in industry. These models are sensitive to data
distribution, thus feature preprocessing, which transforms features from one
distribution to another, is a crucial step to ensure good model quality.
Manually constructing a feature preprocessing pipeline is challenging because
data scientists need to make difficult decisions about which preprocessors to
select and in which order to compose them. In this paper, we study how to
automate feature preprocessing (Auto-FP) for tabular data. Due to the large
search space, a brute-force solution is prohibitively expensive. To address
this challenge, we interestingly observe that Auto-FP can be modelled as either
a hyperparameter optimization (HPO) or a neural architecture search (NAS)
problem. This observation enables us to extend a variety of HPO and NAS
algorithms to solve the Auto-FP problem. We conduct a comprehensive evaluation
and analysis of 15 algorithms on 45 public ML datasets. Overall,
evolution-based algorithms show the leading average ranking. Surprisingly, the
random search turns out to be a strong baseline. Many surrogate-model-based and
bandit-based search algorithms, which achieve good performance for HPO and NAS,
do not outperform random search for Auto-FP. We analyze the reasons for our
findings and conduct a bottleneck analysis to identify the opportunities to
improve these algorithms. Furthermore, we explore how to extend Auto-FP to
support parameter search and compare two ways to achieve this goal. In the end,
we evaluate Auto-FP in an AutoML context and discuss the limitations of popular
AutoML tools. To the best of our knowledge, this is the first study on
automated feature preprocessing. We hope our work can inspire researchers to
develop new algorithms tailored for Auto-FP.

Machine Learning;Artificial Intelligence;Databases;Information Retrieval