Capital One Services, LLC

Esmat Zare

Yasong Zhou

Wayne Decesaris

Mani Afsari

Bhuvaneswari Balasubramanian

Kenneth Cardozo

Landon Nehmer

Venkateshwar Parpelli

Vijaya Pasam

Saira Zaman

Bijun Du

Jitender Jain

Vikrant Khenat

Daniel Nizinski

Tausif Shahid

Methods and systems for extracting and processing data using optical character recognition in real-time environments. For example, the methods and systems provide novel techniques during extracting data using OCR and for a mechanism to process that data. These methods and systems are particularly relevant in real-time environments as the methods and system limit the need for manual review.

Systems and methods for extracting and processing data using optical character recognition in real-time environments

Systems, methods, and apparatuses for determining feature importance of analytics data in predicting a response value include receiving data records, each data record including a response value and values of features associated with the response value; splitting the data records into datasets, each dataset including a part of the data records; generating a machine learning model using each of the datasets, the machine learning model outputting one or more predicting features having influence in predicting the response value for each of the datasets; determining an important feature based on the one or more predicting features; and generating report data indicating that a business metric associated with the important feature has the highest predicted influence among the features on predicting the response value.

Aggregated feature importance for finding influential business metrics

Methods and computer-readable media for repeated holdout validation include 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; generating a plurality of unique seeds; creating a plurality of training sets and a plurality of validation sets; associating each training set with a single validation set; training the 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.

Systems and methods for training machine learning models

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Frisco, TX, United States of America

Esmat Zare