Abstract: Systems and methods of determining and transmitting a code designation are disclosed. A request for a code designation of an item, and information about the item are received. A first probability distribution is obtained via a trained text classification model from the textual description. A second probability distribution is obtained via a trained image classification model, from the one or more images. A plurality of candidate code designations based on the first probability distribution and the second probability distribution are generated. Each candidate code designation includes a first portion and a second portion of the code designation. Respective probabilities of respective candidate code designations in a first subset of the plurality of candidate code designations are aggregated, from which a selected code designation is transmitted to the requesting system as the code designation associated with the item.

Abstract: Systems and methods for training a machine learning (ML) model for predicting probabilities for binary outcomes to automatically generate positive class predictions close to an ideal one probability and negative class predictions close to an ideal zero probability are disclosed. The method includes generating a predictive probability (PP) curve based on a ML algorithm and transforming the PP curve into a curve with probabilities spread close to ideal one probability for positive class predictions (PCP) indicating successful prediction and close to ideal zero probability for negative class predictions (NCP) indicating failed prediction, thereby introducing a valley in the transformed probability curve separating PCP from NCP. The PP curve is transformed by one of (1) minimizing distance between: (a) ideal one probability and PP value of PCP; and (b) ideal zero probability and PP value of NCP, and (2) maximizing distance of PP values from center of PP curve.