Generating training data using sampled values

A system comprising:

• at least one memory device;
• and
• a processing device, operatively coupled with the at least one memory device, to:
• determine a set of data, the set of data comprising a plurality of numerical ranges, a plurality of embeddings, and a plurality of attributes, wherein a numerical range of the plurality of numerical ranges is associated with at least one embedding of the plurality of embeddings and at least one attribute of the plurality of attributes;
• sample the numerical range to obtain a plurality of sample values, wherein a first sample value and a second sample value of the plurality of sample values are different discrete point values sampled from the numerical range and are associated with the at least one embedding and the at least one attribute;
• generate a set of sample value training data comprising a plurality of sample value training vectors, wherein a first sample value training vector of the plurality of sample value training vectors is based on the first sample value, the at least one associated embedding, and the at least one associated attribute and wherein a second sample value training vector of the plurality of sample value training vectors is based on the second sample value, the at least one associated embedding, and the at least one associated attribute;
• mapping the set of sample value training data to a sample value tensor using a feature tensorization map;
• train a neural network prediction model by updating parameters of the neural network prediction model to minimize a loss of an output of the neural network prediction model based on the sample value tensor;
• and
• determine, by the trained neural network prediction model, an output based on a set of input data, the set of input data comprising at least one input embedding and at least one input attribute, wherein the output is a predicted range of values based on an output mean and an output standard deviation.