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AutoML platforms have numerous options for the algorithms to try for eachstep of the analysis, i.e., different possible algorithms for imputation,transformations, feature selection, and modelling. Finding the optimalcombination of algorithms and hyper-parameter values is computationallyexpensive, as the number of combinations to explore leads to an exponentialexplosion of the space. In this paper, we present the SequentialHyper-parameter Space Reduction (SHSR) algorithm that reduces the space for anAutoML tool with negligible drop in its predictive performance. SHSR is ameta-level learning algorithm that analyzes past runs of an AutoML tool onseveral datasets and learns which hyper-parameter values to filter out fromconsideration on a new dataset to analyze. SHSR is evaluated on 284classification and 375 regression problems, showing an approximate 30%reduction in execution time with a performance drop of less than 0.1%.

A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML