View source for 17848728. PROGRAMMATIC SELECTOR FOR CHOOSING A WELL-SUITED STACKED MACHINE LEARNING ENSEMBLE PIPELINE AND HYPERPARAMETER VALUES simplified abstract (Capital One Services, LLC)

=PROGRAMMATIC SELECTOR FOR CHOOSING A WELL-SUITED STACKED MACHINE LEARNING ENSEMBLE PIPELINE AND HYPERPARAMETER VALUES=


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Michael Langford of Plano TX (US)|Michael Langford of Plano TX (US)]][[Category:Michael Langford of Plano TX (US)]]

[[:Category:Jakub Krzeptowski-mucha of Lemont IL (US)|Jakub Krzeptowski-mucha of Lemont IL (US)]][[Category:Jakub Krzeptowski-mucha of Lemont IL (US)]]

[[:Category:Krishna Balam of Henrico VA (US)|Krishna Balam of Henrico VA (US)]][[Category:Krishna Balam of Henrico VA (US)]]

==PROGRAMMATIC SELECTOR FOR CHOOSING A WELL-SUITED STACKED MACHINE LEARNING ENSEMBLE PIPELINE AND HYPERPARAMETER VALUES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17848728 titled 'PROGRAMMATIC SELECTOR FOR CHOOSING A WELL-SUITED STACKED MACHINE LEARNING ENSEMBLE PIPELINE AND HYPERPARAMETER VALUES

==Simplified Explanation==

The patent application describes a stacked machine learning model ensemble pipeline architecture selector that selects the best architecture for a given configuration input and target data set. 

* The selector uses genetic programming to generate and score possible stacked ensemble pipeline architectures.
* It aims to find an architecture that is well-suited for the target data set and conforms to the configuration input.
* The selector converges on an architecture that meets specific scores, evaluation metrics, or other criteria.

Potential applications of this technology:

* Improving the performance of machine learning models by selecting the most suitable ensemble pipeline architecture.
* Automating the process of selecting the best architecture for a given data set and configuration input.
* Enhancing the accuracy and efficiency of machine learning systems.

Problems solved by this technology:

* Selecting the optimal ensemble pipeline architecture can be a time-consuming and complex task.
* Different data sets and configuration inputs require different architectures, making manual selection challenging.
* This technology automates the process and ensures the selection of a well-suited architecture.

Benefits of this technology:

* Saves time and effort by automating the selection process.
* Increases the accuracy and performance of machine learning models by choosing the most suitable architecture.
* Provides a systematic approach to selecting ensemble pipeline architectures for different data sets and configuration inputs.


==Original Abstract Submitted==

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.



[[Category:G06N20/20]]