RUNTIME PREDICTION OF MACHINE LEARNING ALGORITHMS IN AUTOML SYSTEMS

In this paper we introduce a metalearning-based methodology for predicting the training runtime of various machine learning algo- rithms. 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 algo- rithms used in AutoML systems and can be used to enhance their performance in resource-constrained settings.