US 11,941,524 B2
Systems and methods for training machine learning models
Esmat Zare, Frisco, TX (US); Yasong Zhou, Carrollton, TX (US); and Wayne Decesaris, Frisco, TX (US)
Assigned to CAPITAL ONE SERVICES, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Aug. 30, 2022, as Appl. No. 17/899,314.
Application 17/899,314 is a continuation of application No. 16/705,143, filed on Dec. 5, 2019, granted, now 11,461,646.
Prior Publication US 2022/0414471 A1, Dec. 29, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01)
CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
collecting independent data representing independent variables;
collecting dependent data representing a dependent variable;
correlating the independent data with the dependent data;
creating a data set comprising the correlated independent and dependent data;
creating a plurality of training sets and a plurality of validation sets from the data set;
associating each training set with a single validation set;
training a neural network a plurality of times with the training sets and seeds to create a plurality of models;
calculating accuracy metric values for the models using the validation sets associated with the training sets used to create respective models;
performing a statistical analysis of the accuracy metric values; and
ranking the independent variables by a strength of correlation of individual independent variables with the dependent variable, when a metric of the statistical analysis exceeds a threshold, wherein:
the accuracy metric comprises an Area Under the Curve (AUC),
the statistical analysis comprises calculating an average, and
the metric of the statistical analysis comprises the average.