Flexible feature regularization for demand model generation

Abstract

Embodiments forecasting future demand for an item. Embodiments receive a regression based demand algorithm for the item that includes the set of features as regression variables and split the data points into a training set and a testing set. Embodiments assign each of the features of the set of features into one of a plurality of regularization categories and assign a penalty parameter to each of the features subject to regularization. Embodiments train the demand algorithm using the training set, the penalty parameters and the features to generate a trained demand model. Embodiments evaluate the trained demand model using the testing set to determine an early drop metric and repeat the assigning each of the features, the assigning the penalty parameter, the training the demand algorithm and the evaluating the trained demand model until the early drop metric meets a threshold.