公开(公告)号：US10510022B1

公开(公告)日：2019-12-17

申请号：US16451228

申请日：2019-06-25

Applicant: SAS Institute Inc.

Inventor： Ricky Dee Tharrington, Jr. ,  Xin Jiang Hunt ,  Ralph Walter Abbey

IPC: G06N20/00 ,  G06N5/04 ,  G06F17/16 ,  G06F16/245

Abstract: Systems and methods for machine learning, models, and related explainability and interpretability are provided. A computing device determines a contribution of a feature to a predicted value. A feature computation dataset is defined based on a selected next selection vector. A prediction value is computed for each observation vector included in the feature computation dataset using a trained predictive model. An expected value is computed for the selected next selection vector based on the prediction values. The feature computation dataset is at least a partial copy of a training dataset with each variable value replaced in each observation vector included in the feature computation dataset based on the selected next selection vector. Each replaced variable value is replaced with a value included in a predefined query for a respective variable. A Shapley estimate value is computed for each variable.

公开(公告)号：US11922311B2

公开(公告)日：2024-03-05

申请号：US18208455

申请日：2023-06-12

Applicant: SAS Institute Inc.

Inventor： Xinmin Wu ,  Ricky Dee Tharrington, Jr. ,  Ralph Walter Abbey

IPC: G06N3/08 ,  G06N20/00

CPC classification number: G06N3/08 ,  G06N20/00

Abstract: A computing device trains a fair prediction model. A prediction model is trained and executed with observation vectors. A weight value is computed for each observation vector based on whether the predicted target variable value of a respective observation vector of the plurality of observation vectors has a predefined target event value. An observation vector is relabeled based on the computed weight value. The prediction model is retrained with each observation vector weighted by a respective computed weight value and with the target variable value of any observation vector that was relabeled. The retrained prediction model is executed. A conditional moments matrix is computed. A constraint violation matrix is computed. Computing the weight value through computing the constraint violation matrix is repeated until a stop criterion indicates retraining of the prediction model is complete. The retrained prediction model is output.

公开(公告)号：US12093826B2

公开(公告)日：2024-09-17

申请号：US18444906

申请日：2024-02-19

Applicant: SAS Institute Inc.

Inventor： Xinmin Wu ,  Ricky Dee Tharrington, Jr. ,  Ralph Walter Abbey ,  Xin Jiang Hunt

IPC: G06N3/08 ,  G06N5/022

CPC classification number: G06N3/08 ,  G06N5/022

Abstract: A computing device trains a fair prediction model while defining an optimal event cutoff value. (A) A prediction model is trained with observation vectors. (B) The prediction model is executed to define a predicted target variable value and a probability associated with an accuracy of the predicted target variable value. (C) A conditional moments matrix is computed based on fairness constraints, the predicted target variable value, and the sensitive attribute variable value of each observation vector. The predicted target variable value has a predefined target event value only when the probability is greater than a predefined event cutoff value. (D) (A) through (C) are repeated. (E) An updated value is computed for the predefined event cutoff value. (F) (A) through (E) are repeated. An optimal event cutoff value is defined from the predefined event cutoff values used when repeating (A) through (E). The optimal value and prediction model are output.

公开(公告)号：US11775878B2

公开(公告)日：2023-10-03

申请号：US17523607

申请日：2021-11-10

Applicant: SAS Institute Inc.

Inventor： Yan Gao ,  Joshua David Griffin ,  Yu-Min Lin ,  Bengt Wisen Pederson ,  Ricky Dee Tharrington, Jr. ,  Pei-Yi Tan ,  Raymond Eugene Wright

IPC: G06N20/20 ,  G06N7/01

CPC classification number: G06N20/20 ,  G06N7/01

Abstract: A computing device selects new test configurations for testing software. Software under test is executed with first test configurations to generate a test result for each test configuration. Each test configuration includes a value for each test parameter where each test parameter is an input to the software under test. A predictive model is trained using each test configuration of the first test configurations in association with the test result generated for each test configuration based on an objective function value. The predictive model is executed with second test configurations to predict the test result for each test configuration of the second test configurations. Test configurations are selected from the second test configurations based on the predicted test results to define third test configurations. The software under test is executed with the defined third test configurations to generate the test result for each test configuration of the third test configurations.