US 2023/0419189 A1Published application

Programmatic selector for choosing a well-suited stacked machine learning ensemble pipeline and hyperparameter values

AssigneeCapital One Services, LLC

InventorsMichael LANGFORD, Jakub KRZEPTOWSKI-MUCHA, Krishna BALAM

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Abstract

The exemplary embodiments may provide a stacked machine learning model ensemble pipeline architecture selector that selects a well-suited stacked machine learning model ensemble pipeline architecture for a specified configuration input and a target data set. The stacked machine learning model ensemble pipeline architecture selector may generate and score possible stacked machine learning model ensemble pipeline architectures to locate one that is well-suited for the target data set and the conforms with the configuration input. The stacked machine learning model ensemble pipeline architecture selector may use genetic programming to generate successive generations of possible stacked ensemble pipeline architectures and to score those architectures to determine how well-suited they are. In this manner, the stacked machine learning model ensemble pipeline architecture selector may converge on an architecture that is well-suited, for example, that meet one or more scores, evaluation metrics, and/or the like.

Claims20

Dependent claims

A non-transitory computer-readable storage medium for storing instructions that when executed by a processor cause the processor to:

generate a generation of stacked machine learning model ensemble pipeline architectures, wherein each of generated stacked machine learning model ensemble pipeline architectures specifies how many layers of machine learning models there are in the architecture, what machine learning models are on each of the layers and what hyperparameter values are specified for the machine learning models;
apply the generation of stacked machine learning model ensemble pipeline architectures to a data set;
score how well the stacked machine learning model ensemble pipeline architectures in the generation process the data set;
and
repeat at least once:
(1) based on the scores of the stacked machine learning model ensemble pipeline architectures in a most recent generation, select a subset of the stacked machine learning ensemble model pipeline architectures in the previous generation and mutating the stacked machine learning model ensemble pipeline architectures in the previous generation as part of generating a next generation of stacked machine learning model ensemble pipeline architectures, and
(2) score the next generation of stacked machine learning model ensemble pipeline architectures process the data set,
(3) based on the scores for the next generation of stacked machine learning model ensemble pipeline architectures, determine whether to:
repeat steps (1)-(3) with the next generation being the most recent generation, or
select one of stacked machine learning model ensemble pipeline architectures in the next generation that meets an evaluation metric.

7 more elements

A non-transitory computer-readable storage medium for storing instructions that when executed by a processor cause the processor to:

receive as input an indication of what machine learning models may be used in a stacked machine learning model ensemble pipeline architecture;
receive as input an identification of hyperparameters for the machine learning models that may be used in a stacked machine learning model ensemble pipeline architecture;
based on the inputs, generate stacked machine learning model pipeline architectures which contain at least two layers, with each layer including multiple ones of the machine learning models that may be used;
generate possible hyperparameter values for the generated stacked machine learning model pipeline architectures;
score the generated stacked machine learning model pipeline architectures based on a performance with the generated possible hyperparameter values in processing a data set;
and
select one of the generated stacked machine learning model pipeline architectures and a set of generated possible hyperparameter values based on a score associated with each of the generated stacked machine learning model pipeline architectures.

4 more elements

Dependencies are inferred from the claim text.