Stacking for machine learning redshifts applied to SDSS galaxies

TL;DR

This paper demonstrates that stacking machine learning models, including both weak and strong learners, significantly improves photometric redshift estimation for SDSS galaxies with minimal additional computational cost.

Contribution

It introduces and evaluates stacking architectures for photometric redshift estimation, showing consistent improvements across various algorithms and configurations.

Findings

Stacking improves weak learner performance by 1.9% to 21%.

Stacking yields 0.4% to 2.5% improvement for strong learners like AdaBoost.

Stacking benefits are achieved with minimal extra computational effort.

Abstract

We present an analysis of a general machine learning technique called 'stacking' for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift estimate, as output by a base algorithm, back into the same algorithm as an additional input feature in a subsequent learning round. We shown how all tested base algorithms benefit from at least one additional stacking round (or layer). To demonstrate the benefit of stacking, we apply the method to both unsupervised machine learning techniques based on self-organising maps (SOMs), and supervised machine learning methods based on decision trees. We explore a range of stacking architectures, such as the number of layers and the number of base learners per layer. Finally we explore the effectiveness of stacking even when using a successful algorithm such as AdaBoost. We observe a significant improvement of between 1.9% and 21% on all computed metrics when stacking is applied to weak learners (such as SOMs and decision trees). When applied to strong learning algorithms (such as AdaBoost) the ratio of improvement shrinks, but still remains positive and is between 0.4% and 2.5% for the explored metrics and comes at almost no additional computational cost.