Oracle Corporation
METHOD AND SYSTEM FOR TARGET BASED HYPER-PARAMETER TUNING

Abstract:

Techniques are disclosed for tuning hyperparameters of a machine-learning model. A plurality of metrics are selected for which hyperparameters of the machine-learning model are to be tuned. Each metric is associated with a plurality of specification parameters including a target score, a penalty factor, and a bonus factor. The plurality of specification parameters are configured for each metric in accordance with a first criterion. The machine-learning model is evaluated using one or more validation datasets to obtain a metric score. A weighted loss function is formulated based on a difference between the metric score and the target score of each metric, the penalty factor or the bonus factor. The hyperparameters associated with the machine-learning model are tuned in order to optimize the weighted loss function. In response to the weighted loss function being optimized, the machine-learning model is provided as a validated machine-learning model.