Hyperparameter optimization in deep multi-target prediction

TL;DR

This paper introduces an AutoML framework for various multi-target prediction tasks, extending DeepMTP with hyperparameter optimization methods and benchmarking their performance across diverse datasets.

Contribution

It presents the first unified AutoML framework for multiple multi-target prediction settings by integrating HPO into DeepMTP.

Findings

Certain HPO methods outperform others in specific MTP tasks

The framework automates problem selection and model configuration

Benchmark results guide method selection for different datasets

Abstract

As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as classification and regression. Our work can be seen as the first attempt at offering a single AutoML framework for most problem settings that fall under the umbrella of multi-target prediction, which includes popular ML settings such as multi-label classification, multivariate regression, multi-task learning, dyadic prediction, matrix completion, and zero-shot learning. Automated problem selection and model configuration are achieved by extending DeepMTP, a general deep learning framework for MTP problem settings, with popular hyperparameter optimization (HPO) methods. Our extensive benchmarking across different datasets and MTP problem settings identifies cases where specific HPO methods outperform others.