US 11,893,467 B2
Model interpretation
Mark Chan, San Jose, CA (US); Navdeep Gill, San Jose, CA (US); and Patrick Hall, Washington, DC (US)
Assigned to H2O.ai Inc., Mountain View, CA (US)
Filed by H2O.ai Inc., Mountain View, CA (US)
Filed on May 20, 2022, as Appl. No. 17/750,171.
Application 17/750,171 is a continuation of application No. 15/959,030, filed on Apr. 20, 2018, granted, now 11,386,342.
Prior Publication US 2022/0374746 A1, Nov. 24, 2022
Int. Cl. G06N 20/20 (2019.01); G06N 7/00 (2023.01); G06N 20/00 (2019.01); G06F 18/23213 (2023.01); G06F 18/243 (2023.01); G06F 3/01 (2006.01)
CPC G06N 20/20 (2019.01) [G06F 18/23213 (2023.01); G06F 18/24323 (2023.01); G06N 7/00 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
generating a global surrogate model that includes a linear surrogate model and one or more non-linear surrogate models to approximate an output of a machine learning model;
receiving, via a user interface, a selection of a data point included in the linear surrogate model;
in response to the selection of the data point included in the linear surrogate model, updating the one or more non-linear surrogate models to be specific for the selected point;
determining that prediction data associated with the linear surrogate model correlates with predication data associated with machine learning model and prediction data associated with one of the non-linear surrogate models correlates with the prediction data associated with the machine learning model; and
utilizing the linear surrogate model and the one of the non-linear surrogate models to display within the user interface an indication of one or more input features of input data associated with the machine learning model that influenced the prediction data associated with the machine learning model.