18732401. CONSTRUCTING MACHINE LEARNING MODELS simplified abstract (AT&T Intellectual Property I, L.P.)
Contents
CONSTRUCTING MACHINE LEARNING MODELS
Organization Name
AT&T Intellectual Property I, L.P.
Inventor(s)
Jeremy T. Fix of Acworth GA (US)
Robert Woods, Jr. of Plano TX (US)
CONSTRUCTING MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18732401 titled 'CONSTRUCTING MACHINE LEARNING MODELS
The abstract describes a method for initializing a configuration file for a machine learning model based on user input, configuring parameters for feature engineering rules and algorithm definitions, and generating the machine learning model.
- User-requested initialization of a configuration file for a machine learning model
- Configuring feature engineering rules and algorithm definitions based on user-provided values
- Populating the configuration file to generate the machine learning model
Potential Applications: - Customizing machine learning models for specific use cases - Enhancing the performance of machine learning algorithms through user-defined configurations
Problems Solved: - Streamlining the process of configuring machine learning models - Allowing users to tailor machine learning models to their specific needs
Benefits: - Improved model performance through customized configurations - Increased user control and flexibility in machine learning model development
Commercial Applications: Title: Customized Machine Learning Model Configuration for Enhanced Performance Description: This technology can be applied in industries such as healthcare, finance, and marketing to develop tailored machine learning models for specific applications, leading to improved accuracy and efficiency in data analysis and decision-making processes.
Questions about Machine Learning Model Configuration: 1. How does user input impact the configuration of feature engineering rules and algorithm definitions? 2. What are the potential advantages of customizing machine learning models based on user preferences?
Frequently Updated Research: Stay updated on advancements in machine learning model configuration techniques and best practices to optimize model performance and user experience.
Original Abstract Submitted
An example method includes initializing a configuration file for a machine learning model, wherein the initializing is performed in response to receiving a request from a user, and wherein the configuration file comprises a plurality of sections that is configurable by the user, configuring at least one parameter of a feature engineering rules section of the configuration file, wherein the configuring the at least one parameter of the feature engineering rules section is based on a first value provided by the user, configuring at least one parameter of an algorithm definitions section of the configuration file, wherein the configuring the at least one parameter of the algorithm definitions section is based on a second value provided by the user, and populating the configuration file using the feature engineering rules section as configured and the algorithm definitions section as configured, to generate the machine learning model.
