# Towards meta-learning for multi-target regression problems

## TL;DR

This paper develops a meta-learning system to automatically recommend the most suitable multi-target regression method for a given problem, based on a comprehensive meta-dataset and various meta-features, improving prediction accuracy.

## Contribution

It introduces a meta-learning approach with a large synthetic dataset and diverse meta-features to effectively recommend multi-target regression methods.

## Key findings

- Meta-models achieved over 70% balanced accuracy in method recommendation.
- Random Forest meta-model outperformed other meta-learning baselines.
- Meta-learning significantly improves method selection for multi-target regression.

## Abstract

Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. However, none of these methods outperforms the others for all problems. This motivates the development of automatic approachesto recommend the most suitable multi-target regression method. In this paper, we propose a meta-learning system to recommend the best predictive method for a given multi-target regression problem. We performed experiments with a meta-dataset generated by a total of 648 synthetic datasets. These datasets were created to explore distinct inter-targets characteristics toward recommending the most promising method. In experiments, we evaluated four different algorithms with different biases as meta-learners. Our meta-dataset is composed of 58 meta-features, based on: statistical information, correlation characteristics, linear landmarking, from the distribution and smoothness of the data, and has four different meta-labels. Results showed that induced meta-models were able to recommend the best methodfor different base level datasets with a balanced accuracy superior to 70% using a Random Forest meta-model, which statistically outperformed the meta-learning baselines.