International business machines corporation (20240095575). SUFFICIENCY ASSESSMENT OF MACHINE LEARNING MODELS THROUGH MAXIMUM DEVIATION simplified abstract

SUFFICIENCY ASSESSMENT OF MACHINE LEARNING MODELS THROUGH MAXIMUM DEVIATION

Organization Name

international business machines corporation

Inventor(s)

Dennis Wei of Sunnyvale CA (US)

Rahul Nair of Dublin (IE)

Amit Dhurandhar of Yorktown Heights NY (US)

Kush Raj Varshney of Chappaqua NY (US)

Elizabeth Daly of Dublin (IE)

Moninder Singh of Farmington CT (US)

Michael Hind of Cortlandt Manor NY (US)

SUFFICIENCY ASSESSMENT OF MACHINE LEARNING MODELS THROUGH MAXIMUM DEVIATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240095575 titled 'SUFFICIENCY ASSESSMENT OF MACHINE LEARNING MODELS THROUGH MAXIMUM DEVIATION

Simplified Explanation

The abstract of this patent application describes techniques for determining the sufficiency of machine learning models by measuring the maximum deviation of a supervised learning model from a reference model over a certification set.

• Measurement component measures maximum deviation of a supervised learning model from a reference model over a certification set.
• Analysis component determines sufficiency of the supervised learning model based on the maximum deviation.

Potential Applications

This technology can be applied in various fields such as finance, healthcare, marketing, and autonomous systems where accurate and reliable machine learning models are crucial.

Problems Solved

1. Ensuring the reliability and accuracy of machine learning models. 2. Providing a systematic approach to determine the sufficiency of supervised learning models.

Benefits

1. Improved performance and reliability of machine learning models. 2. Enhanced decision-making based on more accurate predictions. 3. Increased trust in machine learning systems.

Potential Commercial Applications

Optimizing marketing strategies, improving healthcare diagnostics, enhancing financial risk assessment, and developing autonomous systems with higher levels of accuracy and reliability.

Possible Prior Art

One possible prior art could be the use of cross-validation techniques to evaluate the performance of machine learning models. Another could be the use of ensemble methods to improve the accuracy of predictions.

How does this technology compare to existing methods for evaluating machine learning models?

This article does not provide a direct comparison to existing methods for evaluating machine learning models. It would be helpful to understand the specific advantages and limitations of this approach compared to traditional techniques.

What are the potential limitations or challenges of implementing this technology in real-world applications?

The article does not address the potential limitations or challenges of implementing this technology in real-world applications. It would be important to consider factors such as computational resources, data availability, and model complexity when applying this approach in practice.

Original Abstract Submitted

techniques regarding determining sufficiency of one or more machine learning models are provided. for example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. the system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in memory. the computer executable components can comprise a measurement component that measures maximum deviation of a supervised learning model from a reference model over a certification set and an analysis component that determines sufficiency of the supervised learning model based at least in part on the maximum deviation.