17972837. COUNTERFACTUAL BACKGROUND GENERATOR simplified abstract (Red Hat, Inc.)
Contents
COUNTERFACTUAL BACKGROUND GENERATOR
Organization Name
Inventor(s)
Robert Geada of Newcastle-upon-Tyne (GB)
Rui Miguel Cardoso De Freitas Machado Vieira of Newcastle-upon-Tyne (GB)
COUNTERFACTUAL BACKGROUND GENERATOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 17972837 titled 'COUNTERFACTUAL BACKGROUND GENERATOR
Simplified Explanation:
The patent application discusses the generation of perturbed seed data values for predictive models by performing perturbation operations. It also involves generating background data values using counterfactual operations and analyzing the predictive model utilizing the background data store.
Key Features and Innovation:
- Generation of perturbed seed data values for predictive models
- Utilization of counterfactual operations to generate background data values
- Analysis of predictive models using background data store
Potential Applications: This technology can be applied in various fields such as:
- Predictive analytics
- Machine learning
- Data science
Problems Solved: The technology addresses the following problems:
- Enhancing the accuracy of predictive models
- Improving the performance of machine learning algorithms
Benefits: The benefits of this technology include:
- Increased precision in predictive modeling
- Enhanced decision-making capabilities
- Improved efficiency in data analysis
Commercial Applications: Potential commercial applications of this technology include:
- Financial forecasting
- Risk assessment
- Marketing analytics
Prior Art: Readers can explore prior art related to this technology in the fields of predictive modeling, machine learning, and data analysis.
Frequently Updated Research: Stay updated on the latest research in predictive modeling, machine learning, and data science to enhance your understanding of this technology.
Questions about the Technology: 1. What are the key advantages of using perturbed seed data values in predictive modeling? 2. How does the utilization of counterfactual operations improve the accuracy of predictive models?
Original Abstract Submitted
A plurality of perturbed seed data values may be generated by performing a plurality of perturbation operations on an initial value to be processed by a predictive model. A plurality of counterfactual operations may be performed to generate a plurality of background data values of a background data store based on respective ones of the plurality of perturbed seed data values, a reference value within a domain of a predictive model, and the predictive model. A model analysis engine may be executed to generate a model analysis of the predictive model utilizing the background data store and the initial value.
