BoostClean: Automated Error Detection and Repair for Machine Learning

TL;DR

BoostClean is an automated system that detects and repairs data errors in machine learning datasets, improving model accuracy by intelligently combining multiple error detection and repair methods using statistical boosting.

Contribution

It introduces BoostClean, a novel ensemble-based approach that automatically selects effective error detection and repair strategies, including a new Word2Vec-based detector, to enhance data quality for ML models.

Findings

BoostClean improves prediction accuracy by up to 9%.

It achieves a 22.2x speedup with optimizations.

Effective error detection across diverse datasets.

Abstract

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.