Wendy A Czika

US Patent:

7127466, Oct 24, 2006

Filed:

Mar 10, 2003

Appl. No.:

10/384841

Inventors:

John C. Brocklebank - Raleigh NC, US
Bruce S. Weir - Raleigh NC, US
Wendy Czika - Raleigh NC, US

Assignee:

SAS Institute Inc. - Cary NC

International Classification:

G06F 17/00

US Classification:

707101, 707 2, 707 5, 707100, 707102

Abstract:

A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

US Patent:

7809539, Oct 5, 2010

Filed:

Dec 6, 2002

Appl. No.:

10/313569

Inventors:

John C. Brocklebank - Raleigh NC, US
Bruce S. Weir - Seattle WA, US
Wendy Czika - Cary NC, US

Assignee:

SAS Institute Inc. - Cary NC

International Classification:

G06G 7/48
G06F 19/00
C12Q 1/00

US Classification:

703 11, 702 20, 435 4

Abstract:

A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

US Patent:

6532467, Mar 11, 2003

Filed:

Apr 10, 2000

Appl. No.:

09/545958

Inventors:

John C. Brocklebank - Raleigh NC
Bruce S. Weir - Raleigh NC
Wendy Czika - Raleigh NC

Assignee:

SAS Institute Inc. - Cary NC

International Classification:

G06F 1730

US Classification:

707100, 707 5, 707 2

Abstract:

A method for selecting node variables in a binary decision tree structure is provided. The binary decision tree is formed by mapping node variables to known outcome variables. The method calculates a statistical measure of the significance of each input variable in an input data set and then selects an appropriate node variable on which to base the structure of the binary decision tree using an averaged statistical measure of the input variable and any co-linear input variables of the data set.

US Patent:

20190370684, Dec 5, 2019

Filed:

May 14, 2019

Appl. No.:

16/411590

Inventors:

- Cary NC, US
Wendy Ann Czika - Cary NC, US
Susan Edwards Haller - Raleigh NC, US
Udo Sglavo - Raleigh NC, US

International Classification:

G06N 20/00

Abstract:

A computing device selects a feature set and hyperparameters for a machine learning model to predict a value for a characteristic in a scoring dataset. A number of training model iterations is determined. A unique evaluation pair is selected for each iteration that indicates a feature set selected from feature sets and a hyperparameter configuration selected from hyperparameter configurations. A machine learning model is trained using each unique evaluation pair. Each trained machine learning model is validated to compute a performance measure value. An estimation model is trained with the feature set, the hyperparameter configuration, and the performance measure value computed for unique evaluation pair. The trained estimation model is executed to compute the performance measure value for each unique evaluation pair. A final feature set and a final hyperparameter configuration are selected based on the computed performance measure value.