17984728. METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DETECTING MODEL PERFORMANCE simplified abstract (Dell Products L.P.)
Contents
- 1 METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DETECTING MODEL PERFORMANCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DETECTING MODEL PERFORMANCE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DETECTING MODEL PERFORMANCE
Organization Name
Inventor(s)
METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DETECTING MODEL PERFORMANCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17984728 titled 'METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DETECTING MODEL PERFORMANCE
Simplified Explanation
Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for detecting model performance. The method may include acquiring a prediction result of an input feature using a target model to determine a confidence of the prediction result. The method may further include reconstructing the input feature using a self-coding model to determine a reconstruction error, the reconstruction error being a difference between the input feature before being reconstructed by the self-coding model and the input feature after being reconstructed by the self-coding model. In addition, the method may include determining a detection result of the target model at least based on a comparison between the confidence and a first threshold and a comparison between the reconstruction error and a second threshold.
- Acquiring prediction result of an input feature using a target model to determine confidence level.
- Reconstructing the input feature using a self-coding model to calculate reconstruction error.
- Determining detection result of the target model based on comparisons between confidence level and a threshold, and between reconstruction error and another threshold.
Potential Applications
This technology can be applied in various fields such as:
- Anomaly detection in data analysis
- Quality control in manufacturing processes
- Fraud detection in financial transactions
Problems Solved
This technology helps in:
- Improving model performance evaluation
- Enhancing accuracy in prediction results
- Identifying potential errors or anomalies in the input data
Benefits
The benefits of this technology include:
- Increased reliability of model predictions
- Better understanding of model confidence levels
- Improved decision-making based on accurate detection results
Potential Commercial Applications
With its ability to enhance model performance evaluation, this technology can be utilized in:
- Financial institutions for fraud detection systems
- Healthcare industry for anomaly detection in patient data
- E-commerce platforms for quality control in product recommendations
Possible Prior Art
One possible prior art in this field is the use of autoencoders for anomaly detection in machine learning models. Autoencoders have been used to reconstruct input data and detect anomalies based on reconstruction errors.
Unanswered Questions
How does this technology compare to existing methods for model performance evaluation?
This article does not provide a direct comparison with existing methods for model performance evaluation. It would be interesting to know the specific advantages or limitations of this technology compared to traditional approaches.
What are the computational requirements for implementing this method on a large scale?
The article does not address the computational resources needed to implement this method on a large scale. Understanding the computational demands can help in assessing the feasibility of deploying this technology in real-world applications.
Original Abstract Submitted
Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for detecting model performance. The method may include acquiring a prediction result of an input feature using a target model to determine a confidence of the prediction result. The method may further include reconstructing the input feature using a self-coding model to determine a reconstruction error, the reconstruction error being a difference between the input feature before being reconstructed by the self-coding model and the input feature after being reconstructed by the self-coding model. In addition, the method may include determining a detection result of the target model at least based on a comparison between the confidence and a first threshold and a comparison between the reconstruction error and a second threshold.
