  Predictive models based on machine learning can be highly sensitive to data
error. Training data are often combined with a variety of different sources,
each susceptible to different types of inconsistencies, and new data streams
during prediction time, the model may encounter previously unseen
inconsistencies. An important class of such inconsistencies is domain value
violations that occur when an attribute value is outside of an allowed domain.
We explore automatically detecting and repairing such violations by leveraging
the often available clean test labels to determine whether a given detection
and repair combination will improve model accuracy. We present BoostClean which
automatically selects an ensemble of error detection and repair combinations
using statistical boosting. BoostClean selects this ensemble from an extensible
library that is pre-populated general detection functions, including a novel
detector based on the Word2Vec deep learning model, which detects errors across
a diverse set of domains. Our evaluation on a collection of 12 datasets from
Kaggle, the UCI repository, real-world data analyses, and production datasets
that show that Boost- Clean can increase absolute prediction accuracy by up to
9% over the best non-ensembled alternatives. Our optimizations including
parallelism, materialization, and indexing techniques show a 22.2x end-to-end
speedup on a 16-core machine.
