System and method for score normalization

A system for mapping a first set of output values of a first learning model to a distribution of a second set of output values of a second learning model, the system comprising:

• at least one processor;
• and
• a memory containing instructions which, when executed by the at least one processor, cause the at least one processor to:
• calculate a first distribution function for a set of source values;
• calculate a second distribution function for a set of target values;
• calculate a first set of quantiles for the first distribution function, and a second set of quantiles for the second distribution function;
• use said first and second set of quantiles in a linear interpolation of the set of target values to obtain a source values array and a matched interpolated values array;
• calculate an absolute distance from each value in the source values array to the first set of output values of the first learning model;
• determine a corresponding value in the matched interpolated values array corresponding to a value in the source values array which has the smallest said absolute distance;
• and
• output a set of matched values,
• wherein the first and second learning models are one of:
• the same type, or a different type, and
• wherein the type is one of:
• a convolutional neural network (CNN), a support vector machine (SVM), k-nearest neighbor (KNN), generative additive model (GAM), a decision tree, neural network, or deep neural network.