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.

Abstract: A method for evaluating a relative contribution of a first group of J data sets in a collection of N data sets, wherein N>J, includes first applying the collection of N data sets and second applying the first group of J data sets to a model and generating one or more observations O on the collection of N data sets and the first group of J data sets, including generating a N NSA curve comprising computing, using the model, an observation ON on the collection of N data sets; and generating a N?J NSA curve for the first group of J data sets by removing the first group of J data sets from the collection of N data sets, and generating, using the model, an observation ON?J with the first group of J data sets removed. The method then includes generating a measure MJ of contributions of the group of J data sets based on the N NSA curve and the N?J NSA curves.

Abstract: A method for evaluating a relative contribution of a first group of J data sets in a collection of N data sets, wherein N>J, includes first applying the collection of N data sets and second applying the first group of J data sets to a model and generating one or more observations O on the collection of N data sets and the first group of J data sets, including generating a N NSA curve comprising computing, using the model, an observation ON on the collection of N data sets; and generating a N?J NSA curve for the first group of J data sets by removing the first group of J data sets from the collection of N data sets, and generating, using the model, an observation ON?J with the first group of J data sets removed. The method then includes generating a measure MJ of contributions of the group of J data sets based on the N NSA curve and the N?J NSA curves.

Abstract: A data evaluation method includes a processor receiving data sets N, each of which has one or more parameters, applying the data sets N to a machine learning model and generating observations on the data sets N; and executing a network sensitivity analysis (NSA) that includes generating a N NSA curve for each of k distinct parameters in the N data sets including computing an observation ON with the data sets, and generating a N?j NSA curve for each of the of the k distinct parameters. Generating a N?J NSA curve includes removing the jth data set from the N data sets and computing an observation ONj. Executing the NSA further includes determining a contribution of a jth data set based on the k N NSA curves and the k N?j NSA curves; and computing a relative strength Sj of each of the N data sets.