  The goal of automated feature generation is to liberate machine learning
experts from the laborious task of manual feature generation, which is crucial
for improving the learning performance of tabular data. The major challenge in
automated feature generation is to efficiently and accurately identify
effective features from a vast pool of candidate features. In this paper, we
present OpenFE, an automated feature generation tool that provides competitive
results against machine learning experts. OpenFE achieves high efficiency and
accuracy with two components: 1) a novel feature boosting method for accurately
evaluating the incremental performance of candidate features and 2) a two-stage
pruning algorithm that performs feature pruning in a coarse-to-fine manner.
Extensive experiments on ten benchmark datasets show that OpenFE outperforms
existing baseline methods by a large margin. We further evaluate OpenFE in two
Kaggle competitions with thousands of data science teams participating. In the
two competitions, features generated by OpenFE with a simple baseline model can
beat 99.3% and 99.6% data science teams respectively. In addition to the
empirical results, we provide a theoretical perspective to show that feature
generation can be beneficial in a simple yet representative setting. The code
is available at https://github.com/ZhangTP1996/OpenFE.
