Abstract: A system includes a memory storing a dimensionally aware model generated based on a training set and guided by feature dimensions and instructions for execution a processor. The instructions include, in response to receiving a set of data from a user device, identifying a set of features from the set of data and applying the dimensionally aware model to the set of features by implementing a boundary representation.

Abstract: An embodiment involves a hybrid machine learning classifier that uses a random forest of decision tree classifiers to predict a tariff code prefix, and uses a plurality of expert trees to predict a tariff code suffix from properties related to chemical components associated with the respective tariff code prefixes. The embodiment also involves: determining a proportion of a dominant chemical component in comparison to other chemical components in a new set of chemical components; calculating similarity scores for the new set of chemical components and words associated with the tariff code prefixes; generating a feature vector from the proportion and the similarity scores; and obtaining a predicted tariff code including a predicted tariff code prefix determined by applying the random forest to the feature vector, and a predicted tariff code suffix determined by traversing a particular expert tree in accordance with properties related to the new set of chemical components.

Abstract: A system includes at least one processor and a memory. The memory stores a dimensionally aware model generated based on a training set and guided by feature dimensions and instructions for execution by the at least one processor. The instructions include, in response to receiving a set of data from a user device, identifying a set of features from the set of data and applying the dimensionally aware model to the set of features by implementing a boundary representation. The instructions include classifying the set of features as acceptable in response to the implementation of the boundary representation indicating the set of features are outside the boundary representation, classifying the set of features as unacceptable in response to the implementation of the boundary representation indicating the set of features are inside the boundary representation, and generating, for display on the user device, an alert based on the classification.