Patent ID: 11858651
Assignee: THE BOEING COMPANY
Field: Computer technology (Electrical engineering)
Classification: CPC B  G | IPC B  G

Claim 6:
7. A method of interactive machine learning model development, the method comprising:
executing an application, via processing circuitry, to generate a visual environment including a graphical user interface (GUI) for interactive development of a machine learning model, according to an iterative process at least an iteration of which includes at least:
accessing a plurality of observations of data of a system, each of the plurality of observations of the data including values of a plurality of independent variables, and a value of a dependent variable;
visually presenting, via the GUI, the plurality of independent variables;
receiving user input indicating selection of a set of independent variables from the plurality of independent variables;
intelligently performing imputation on the set of independent variables to add values of independent variables of interest to the set of independent variables and/or intelligently performing cleansing on the set of independent variables to remove values of independent variables not of interest from the set of independent variables;
generating a data quality table that tracks values of independent variables that are added via imputation or removed via cleansing at each iteration of the iterative process;
visually presenting, via the GUI, infographics visually summarizing and comparing independent variables in the set of independent variables, wherein the infographics include the data quality table;
receiving user input indicating selection of a refined set of independent variables from the set of independent variables;
performing an interactive feature construction by transforming the refined set of independent variables into a set of features for use in building the machine learning model to predict the dependent variable; and
building the machine learning model using a machine learning algorithm, the set of features generated from the refined set of independent variables, and a training set produced from the set of features and the plurality of observations of the data; and

outputting the machine learning model for deployment to predict and thereby produce predictions of the dependent variable for additional observations of the data that exclude the value of the dependent variable, the predictions produced by the machine learning model being more accurate than produced by a corresponding machine learning model built without the interactive feature construction and selection that include user input via the GUI.