Patent ID: 11868854
Assignee: ORACLE INTERNATIONAL CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform:
for each training dataset of a plurality of training datasets:
a) deriving, from the training dataset, a plurality of values for a plurality of dataset metafeatures;
b) generating for a machine learning (ML) model that has a plurality of hyperparameters:
i) a plurality of landmark hyperparameter configurations that comprises a first landmark hyperparameter configuration and a second landmark hyperparameter configuration, and
ii) a plurality of distinct hyperparameter configurations that includes the plurality of landmark hyperparameter configurations, wherein each hyperparameter configuration of the plurality of distinct hyperparameter configurations contains a value for each hyperparameter of the plurality of hyperparameters; and

c) for each hyperparameter configuration of the plurality of distinct hyperparameter configurations:
i) configuring the ML model with the hyperparameter configuration;
ii) training, based on the training dataset, the ML model when configured with the hyperparameter configuration;
iii) obtaining an empirical quality score that indicates how effective was said training the ML model when configured with the hyperparameter configuration; and
iv) generating a performance tuple of a plurality of performance tuples, wherein the performance tuple contains: the hyperparameter configuration, the plurality of values for the plurality of dataset metafeatures, and the empirical quality score;

encoding each performance tuple of the plurality of performance tuples into a respective feature vector that contains:
the empirical quality score of the first landmark hyperparameter configuration, and
the empirical quality score of the second landmark hyperparameter configuration;

training, based on said feature vectors of the plurality of performance tuples, a regressor to predict an estimated quality score based on a given dataset and a given hyperparameter configuration;
deriving, from a new dataset, a new plurality of values for said plurality of dataset metafeatures;
for each hyperparameter configuration of a second plurality of distinct hyperparameter configurations, predicting, by the regressor, a new estimated quality score of a first plurality of estimated quality scores, wherein the new estimated quality score is based on: the hyperparameter configuration, and the new plurality of values for the plurality of dataset metafeatures;
selecting a particular hyperparameter configuration of the second plurality of distinct hyperparameter configurations that has a highest estimated quality score of the first plurality of estimated quality scores; and
training the ML model based on the particular hyperparameter configuration and the new dataset.