Abstract: A disclosed method may include iterating a model optimization process, the iterating including one or more iterations. The method may also include updating a classification model based on the iterating, the updating performed using training data. The method may further include generating a final version of the classification model based on a final iteration. The method may also include setting a parameter (q), the parameter corresponding to a total number of observations (Q) that are to be removed from the training data by the final iteration. The method may further include determining one or more corresponding numbers of observations to remove from the training data, where the corresponding number of observations are to be removed at some of select iterations tk, and the corresponding number of observations are to be removed based on the number Q and an estimate of the number of iterations remaining until the final iteration.

Abstract: A device receives selection of a classification and regression framework, and receives training data for the classification and regression framework. The device applies the training data to the classification and regression framework to generate a trained model, and monitors performance of the trained model. The device inspects a structure of the trained model, and reduces a size of the trained model. The device generates an object based on the trained model, and provides the object for display.

Abstract: Systems and methods for determining a reduced feature set for a model for classifying data are disclosed. In some embodiments, the method includes obtaining a first feature set for the model. The method may also include selecting a second feature set for the model, wherein the second feature set is a candidate for the reduced feature set. In some embodiments, the second feature set is a subset or a superset of the first feature set. In some embodiments, the selection includes applying a selection statistical test. The method may further include determining whether the model using the second feature set in place of the first feature set is adequate for classifying the data. In some embodiments, the determination includes applying an evaluation statistical test.