Initialize optimized parameter in data processing system

An information handling system comprising:

• one or more processors;
• a memory coupled to at least one of the processors;
• a set of computer program instructions stored in the memory and executed by at least one of the processors in order to perform actions of:
• loading a machine learning model and a set of test case statistical data into a user system, wherein the set of test case statistical data is based on a set of test cases corresponding to the machine learning model and comprises a plurality of input parameter sets and a corresponding set of output quality measurements, and wherein the machine learning model is built by:
• transforming univariate and bivariate statistics into a scaled format such that the statistics are combined through feature scaling, wherein transforming the univariate and bivariate statistics comprises:
• sorting scaled statistic values and counting the values within predefined intervals to form fixed-length statistical distribution vectors, wherein the scaled statistic values are processed so that number of values falling within each predefined equal-width interval are determined in a manner that enables a uniform representation of data characteristics;
• and
• building the machine learning model based on the transformed univariate and bivariate statistics, and the plurality of input parameter sets;
• comparing user data comprising a second transformed univariate and bivariate statistics on the user system against the set of test case statistical data, wherein the comparison identifies one of the plurality of input parameter sets to optimize the machine learning model based on the set of output quality measurements;
• generating, at the user system, an optimized machine learning model using the machine learning model and the identified input parameter set;
• displaying one or more similar sets of test data on a user interface;
• responsive to receiving through the user interface a selection choice from the one or more similar sets of test data, applying the selection choice to the machine learning model and predicting an accuracy of the optimized machine learning model;
• outputting the optimized machine learning model to the user through the user interface;
• and
• responsive to receiving acceptance of the optimized machine learning model through the user interface executing the optimized machine learning model and refreshing the optimized machine learning model, by collecting information to add a new pre-trained machine learning model.

A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising:

• loading a machine learning model and a set of test case statistical data into a user system, wherein the set of test case statistical data is based on a set of test cases corresponding to the machine learning model and comprises a plurality of input parameter sets and a corresponding set of output quality measurements, and wherein the machine learning model is built by:
• transforming univariate and bivariate statistics into a scaled format such that the statistics are combined through feature scaling, wherein transforming the univariate and bivariate statistics comprises:
• sorting scaled statistic values and counting the values within predefined intervals to form fixed-length statistical distribution vectors, wherein the scaled statistic values are processed so that number of values falling within each predefined equal-width interval are determined in a manner that enables a uniform representation of data characteristics;
• and
• building the machine learning model based on the transformed univariate and bivariate statistics, and the plurality of input parameter sets;
• comparing user data comprising a second transformed univariate and bivariate statistics on the user system against the set of test case statistical data, wherein the comparison identifies one of the plurality of input parameter sets to optimize the machine learning model based on the set of output quality measurements;
• generating, at the user system, an optimized machine learning model using the machine learning model and the identified input parameter set;
• displaying one or more similar sets of test data on a user interface;
• responsive to receiving through the user interface a selection choice from the one or more similar sets of test data, applying the selection choice to the machine learning model and predicting an accuracy of the optimized machine learning model;
• outputting the optimized machine learning model to the user through the user interface;
• and
• responsive to receiving acceptance of the optimized machine learning model through the user interface executing the optimized machine learning model and refreshing the optimized machine learning model, by collecting information to add a new pre-trained machine learning model.