17933136. SYSTEMS AND METHODS FOR EVALUATING COUNTERFACTUAL SAMPLES FOR EXPLAINING MACHINE LEARNING MODELS simplified abstract (Capital One Services, LLC)
Contents
- 1 SYSTEMS AND METHODS FOR EVALUATING COUNTERFACTUAL SAMPLES FOR EXPLAINING MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR EVALUATING COUNTERFACTUAL SAMPLES FOR EXPLAINING MACHINE LEARNING MODELS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
SYSTEMS AND METHODS FOR EVALUATING COUNTERFACTUAL SAMPLES FOR EXPLAINING MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Brian Barr of Schenectady NY (US)
SYSTEMS AND METHODS FOR EVALUATING COUNTERFACTUAL SAMPLES FOR EXPLAINING MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17933136 titled 'SYSTEMS AND METHODS FOR EVALUATING COUNTERFACTUAL SAMPLES FOR EXPLAINING MACHINE LEARNING MODELS
Simplified Explanation
The computing system described in the patent application trains a machine learning model to classify samples of a training dataset and generates counterfactual samples to improve the model's performance. It determines the distance between the training dataset and each counterfactual sample, recommending the use of the counterfactual sample with the smallest distance as a potential improvement to the model.
- The computing system trains a machine learning model to classify samples of a training dataset.
- It generates counterfactual samples to enhance the model's performance.
- The system calculates the distance between the training dataset and each counterfactual sample.
- Based on the distance scores, it recommends using the counterfactual sample with the smallest distance as an improvement to the model.
Potential Applications
This technology could be applied in various fields such as healthcare, finance, and marketing for improving the accuracy and performance of machine learning models.
Problems Solved
1. Enhances the performance of machine learning models by generating and utilizing counterfactual samples. 2. Provides recommendations for improving model accuracy based on distance scores between training data and counterfactual samples.
Benefits
1. Increased accuracy and efficiency of machine learning models. 2. Enhanced decision-making capabilities in various industries. 3. Potential for better predictions and insights from data analysis.
Potential Commercial Applications
Improving recommendation systems in e-commerce, enhancing fraud detection in financial services, optimizing personalized healthcare treatments, and refining targeted marketing strategies.
Possible Prior Art
One possible prior art could be the use of counterfactual samples in machine learning to improve model performance, but the specific method of determining distance scores between the training dataset and counterfactual samples for recommendation purposes may be novel.
Unanswered Questions
How does the computing system generate the counterfactual samples?
The process of generating counterfactual samples is not detailed in the abstract. It would be interesting to know the specific methodology used for creating these samples.
What types of machine learning models are compatible with this technology?
The abstract does not specify the types of machine learning models that can benefit from this approach. Understanding the compatibility of different models would be valuable for implementation in various applications.
Original Abstract Submitted
In some aspects, a computing system may train a machine learning model to classify a plurality of samples of a training dataset. The computing system may generate a plurality of counterfactual samples. The computing system may determine a distance score between the training dataset and a first counterfactual sample of the plurality of counterfactual samples. Based on determining that the distance between the training dataset and the first counterfactual sample is smaller than other distances corresponding to other counterfactual samples of the plurality of counterfactual samples, the computing system may generate a recommendation to use the first counterfactual sample.
