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

Claim 16:
17. One or more computers comprising one or more hardware processors configured to:
for each training dataset of a plurality of training datasets:
a) derive, from the training dataset, a plurality of values for a plurality of dataset metafeatures;
b) generate fora 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) configure the ML model with the hyperparameter configuration;
ii) train, based on the training dataset, the ML model when configured with the hyperparameter configuration;
iii) obtain an empirical quality score that indicates how effective was said training the ML model when configured with the hyperparameter configuration; and
iv) generate 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;

encode 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;

train, 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;
derive, 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, predict, by the regressor, a new estimated quality score of a 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;
select a particular hyperparameter configuration of the second plurality of distinct hyperparameter configurations that has a highest estimated quality score of the plurality of estimated quality scores; and
train the ML model based on the particular hyperparameter configuration and the new dataset.