RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines.

Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach

Research output: Working paper/PreprintPreprint

Abstract

The explosion of digital data has created multiple opportunities for organizations and individuals to leverage machine learning (ML) to transform the way they operate. However, the shortage of experts in the field of machine learning -- data scientists -- is often a setback to the use of ML. In an attempt to alleviate this shortage, multiple approaches for the automation of machine learning have been proposed in recent years. While these approaches are effective, they often require a great deal of time and computing resources. In this study, we propose RankML, a meta-learning based approach for predicting the performance of whole machine learning pipelines. Given a previously-unseen dataset, a performance metric, and a set of candidate pipelines, RankML immediately produces a ranked list of all pipelines based on their predicted performance. Extensive evaluation on 244 datasets, both in regression and classification tasks, shows that our approach either outperforms or is comparable to state-of-the-art, computationally heavy approaches while requiring a fraction of the time and computational cost.
Original languageEnglish
Volumeabs/1911.00108
StatePublished - 2019

Publication series

NameArxiV cs.ML

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