MACHINE LEARNING VALIDATION AND ACCURACY

1 . A method comprising, by a computing device:

accessing a definition of a machine-learning model and its corresponding training configurations;

adjusting the definition of the machine-learning model to downscale the machine-learning model;

modifying one or more of the corresponding training configurations to reduce complexity of training the machine-learning model;

performing a downscaled training of the machine-learning model of the adjusted definition using the modified corresponding training configurations;

determining that one or more errors caused by either the non-adjusted definition of the machine-learning model or non-modified corresponding training configurations occur based on a return code of the downscaled training; and

causing, in response to the determination, a training of the machine-learning model of non-adjusted definition using the non-modified corresponding training configurations to be aborted.

2 . A method comprising, by a computing system:

determining a first set of bins that collectively cover a pre-determined numerical range with each bin covering a sub-range of the pre-determined range;

determining a second set of bins that collectively cover the pre-determined range, wherein each of the second set of bins covers a different but overlapping sub-range with respect to a corresponding bin in the first bin set;

accessing a value that falls within the pre-determined range;

determining that the value falls within a first bin of the first bin set and a second bin of the second bin set;

determining a positive value for each the first and second bins, wherein the positive values indicates an association level of the value with the first and second bins; and

determining a representation of the value based on a weighted average of associated vectors determined based on the positive values.