Computer-implemented methods and systems for quantifying appropriate machine learning model complexity corresponding to training dataset are provided. The method comprises monitoring, using one or more processors, N observed variables, vi through v<sub>N</sub>, of a training dataset for a machine learning model; translating the N observed variables into m equisized bin indexes which generate m<sup>N</sup> possible equisized hypercells to estimate a fundamental dimensionality for the dataset; generating one or more samples by assigning a record in the dataset with numbers j through k as set id; generating a merged sample Si, for one or more values of the set id i, where i goes from j to k; and computing a fractal dimension of the equisized hypercube phase space based on count of cells with data coverage of at least one data point.
LATENT FEATURE DIMENSIONALITY BOUNDS FOR ROBUST MACHINE LEARNING ON HIGH DIMENSIONAL DATASETS