Systems and methods for detecting data drift for data used in machine learning models

• US 10,599,957 B2
• Filed: 10/26/2018
• Issued: 03/24/2020
• Est. Priority Date: 07/06/2018
• Status: Active Grant

• Current Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Original Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Inventors: Walters, Austin, Goodsitt, Jeremy, Truong, Anh, Abdi Taghi Abad, Fardin, Watson, Mark, Pham, Vincent, Key, Kate, Farivar, Reza
• Primary Examiner(s): Nilsson, Eric
• Application Number:
  US16/172,344
• Publication Number:
  US 20200012900A1
• Time in Patent Office: 515 Days
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Abstract

A system and method for detecting data drift is disclosed. The system may be configured to perform a method, the method including receiving model training data and generating a predictive model. Generating the predictive model may include model training or hyperparameter tuning. The method may include receiving model input data and generating predicted data using the predictive model, based on the model input data. The method may include receiving event data and detecting data drift based on the predicted data and the event data. The method may include receiving current data and detecting data drift based on the data profile of the current data. The method may include model training and detecting data drift based on a difference in a trained model parameter from a baseline model parameter. The method may include hyperparameter tuning and detecting data drift based on a difference in a tuned hyperparameter from a baseline hyperparameter. The method may include correcting the model based on the detected data drift.

Claims

1. A system for detecting data drift, the system comprising:

one or more memory units for storing instructions; and

one or more processors configured to execute the instructions to perform operations comprising;

receiving model training data;

generating a predictive model;

receiving model input data;

generating predicted data using the predictive model, based on the model input data;

receiving event data,

detecting data drift based on a comparison of a data profile of the predicted data to a data profile of the event data; and

correcting the model based on the determined drift.

2. The system of claim 1, the operations comprising:

generating a predicted data covariance matrix based on the predicted data; and

generating an event data covariance matrix based on the event data;

wherein determining data drift is based on a comparison of the predicted data covariance matrix to the event data covariance matrix.

3. The system of claim 2, wherein:

the model input data comprises a plurality of model input datasets, each dataset comprising data associated with a different scenario.

4. The system of claim 1, wherein the model is one of a recurrent neural network model, a deep learning model, or a convolutional neural network model.

5. The system of claim 1, wherein generating a model comprises generating a development instance to generate the model.

6. The system of claim 1, the operations comprising generating a development instance in response to the received event data;

wherein detecting data drift is performed by the development instance.

7. The system of claim 1, wherein generating the predictive model comprises training the predictive model, wherein training the model terminates upon satisfaction of a termination condition.

8. The system of claim 7, wherein the termination condition is based on a performance metric of the predictive model, an elapsed time, a number of predictive models trained, a number of epochs, an accuracy score, or a rate of change of an accuracy score.

9. The system of claim 1, wherein generating the predictive model comprises hyperparameter tuning of the predictive model.

10. The system of claim 9, wherein the hyperparameter tuning comprises:

adjusting a hyperparameter of the predictive model;

training the predictive model using the adjusted hyperparameter;

determining a performance metric of the trained predictive model; and

terminating hyperparameter tuning if the performance metric meets or exceeds a threshold.

11. The system of claim 10, wherein the adjusted hyperparameter comprises at least one of a number of layers in a neural network, an activation function for a neural network node, or a filter in a convolutional neural network.

12. The system of claim 1, wherein the predicted data is stock market data, rainfall data, transaction data, climate data, health data, educational outcome data, demographic data, sales data, or poll data.

13. The system of claim 1, wherein correcting the predictive model comprises at least one of training the predictive model based on the event data or performing a hyperparameter tuning of the predictive model based on the event data.

14. The system of claim 1, wherein the model input data comprises a mix of actual data and synthetic data.

15. The system of claim 1, wherein the model input data comprises data of a first category, and the predicted data comprises data of a first category.

16. The system of claim 15, wherein the model input data comprise data of a second category, and the predicted data does not comprise data of the second category.

17. The system of claim 1, wherein detecting data drift is based on at least one of a Mean Absolute Error method, a Root Mean Squared Error method, or a percent good classification method.

18. A method for detecting data drift, the method comprising:

receiving model training data;

generating a predictive model;

receiving model input data;

generating predicted data using the predictive model, based on the model input data;

receiving event data,

detecting data drift based on a comparison of a data profile of the predicted data to a data profile of the event data; and

correcting the model based on the determined drift.

19. A system for updating a model, the system comprising one or more memory units for storing instructions;

and

one or more processors configured to execute the instructions to perform operations comprising;

receiving a modeling request associated with a user;

providing a predictive model based on the modeling request;

generating predicted data using the model, the predicted data being of a data category;

receiving event data, the event data being of the same data category as the predicted data;

detecting data drift based on at least one of;

a comparison of a data profile of a predicted data to the data profile of the event data;

or

a comparison of a predicted data covariance matrix to an event data covariance matrix;

updating the predictive model based on the detected data drift; and

transmitting a notification that the predictive model has been updated.