Runtime Prediction of Machine Learning Algorithms in AutoML Systems

Abstract

In this paper we introduce a metalearning-based methodology for predicting the training runtime of various machine learning algorithms. This prediction is important for automated machine learning (AutoML) systems because they search by training and evaluating a large number of machine learning models in order to identify the best model for a given dataset. Our approach identifies the main factors that impact the runtime performance of state of the art algorithms used in AutoML systems and can be used to enhance their performance in resource-constrained settings.