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Abstract

A system, program product, and method for performing multi-objective automated machine learning. The method includes selecting two or more objectives from a plurality of objectives to be optimized and injecting data and the objectives into a first machine learning (ML) pipeline. The first ML pipeline includes one or more data transformation stages in communication with a modeling stage. The method also includes executing, subject to the injecting, optimization of the two or objectives. Such executing includes selecting a respective algorithm for each of the data transformation stages and the modeling stage. Each respective algorithm is associated with a first set of respective hyperparameters. The executing also includes generating a plurality of second ML pipelines. Each second ML pipeline defines a Pareto-optimal solution of the two or more objectives, thereby defining a plurality of Pareto-optimal solutions, The executing also includes selecting one Pareto-optimal solution from the plurality of Pareto-optimal solutions.

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