Abstract: A computer-implemented method to establish a relative importance of an input parameter pj in a plurality of input parameters pi in a data set input to a machine learning model, the data set represented by a j row by k column matrix Im, an intersection of each row with each column defining an element, the method includes for each of the plurality of parameters pi in the input data set, a computer sorts columns ki of the matrix Im. to produce a re-ordered matrix Im,j; the computer determines a hyper-parameter N* of sub-matrices into which may be sorted the values in a jth row of the re-ordered matrix Im,j; the computer generates a plurality of group sub-matrices Gi, each of the group sub-matrices comprising a subset of columns and the jth row; the computer inputs the re-ordered matrix Im,j into a fully-trained machine learning model to produce machine learning model outputs; and the computer produces normalized mean values of the outputs.