Automated feature generation, selection and hyperparameter tuning from structured data for supervised learning problems

A configuration system comprising one or more processors which, alone or in combination, are configured to provide for performance of the following steps:

• performing feature generation in a feature space having a plurality of features using at least one predefined process for a plurality of data types;
• identifying a minimum set of relevant features;
• decreasing the feature space using at least one filtering approach and the minimum set of relevant features;
• and
• devising a Bayesian combinatorial optimization heuristic to jointly identify a feature subset and a hyperparameter setting for a given query, a machine learning algorithm, and a dataset.