Patent ID: 11928182
Assignee: AMAZON TECHNOLOGIES, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system, comprising:
one or more computing devices;
wherein the one or more computing devices include instructions that upon execution on or across the one or more computing devices cause the one or more computing devices to:
obtain an indication of a machine learning task which includes prediction of at least a first target variable corresponding to respective input records;
select a first iterative training strategy to be used for the machine learning task from a plurality of iterative training strategies, based at least in part on (a) respective sizes of labeled and unlabeled data sets available for training one or more models for the machine learning task and (b) an indication of availability of one or more derived features corresponding to individual ones of the records of the labeled data set, wherein a particular iterative training strategy of the plurality of iterative training strategies differs from another iterative training strategy of the plurality of iterative training strategies in a type of machine learning model utilized for stacking;
implement a plurality of training iterations of the first iterative training strategy until an iteration termination criterion is met, wherein a particular training iteration comprises:
selecting a batch of unlabeled records from the unlabeled data set;
generating, using at least a portion of the labeled data set, a first version of a stacking model from a second version of the stacking model, wherein the second version corresponds to a previous training iteration;
obtaining, from the first version of the stacking model, respective proposed labels corresponding to individual records of the batch of unlabeled records; and
generating, using the records of the batch and the respective proposed labels, respective first versions of one or more base models from second versions of the one or more base models corresponding to the previous training iteration, wherein input provided to the second versions of the one or more base models to generate their respective first versions does not include records of the labeled data set;

store, after the iteration termination criterion is met, a final trained ensemble of models including the stacking model and at least one base model, wherein the final trained ensemble of models is obtained from a selected iteration of the plurality of training iterations; and
obtain, using at least a portion of the final trained ensemble of models, a predicted value of the first target variable corresponding to an input record which was not in the unlabeled data set.