Model Selection via Usefulness Scores for Unlabeled Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users lack the resources to select the most accurate machine learning model for their specific tasks, as they often only have unlabeled datasets and cannot compare outputs from multiple models effectively.
Innovation Solution
A method and system that utilize a server to acquire unlabeled datasets and candidate models from different providers, generate usefulness scores based on label-wise probabilities, entropy scores, and agreement scores to select the most accurate model for generating labels, and facilitate the selection and usage of the target model for the dataset provider.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If buyers only have unlabeled datasets, then they cannot compare outputs from multiple models to determine accuracy, but they still need to select the most accurate model for their task
Solution Approach 1:
The patent introduces an intermediary mechanism (usefulness score calculation system) that mediates between the unlabeled dataset and the candidate models. This system generates synthetic labels through multiple candidate models and uses agreement scoring to create a reliable selection criterion without requiring actual labeled data from the buyer.
Solution Approach 2:
The patent implements a feedback loop where candidate models generate predictions on the unlabeled dataset, these predictions are compared across models to calculate agreement scores, and this feedback information is used to determine the usefulness score that guides model selection. The system continuously refines its assessment based on model performance feedback.
2Ease of operation
If users lack specialized resources for training models, then they cannot obtain effective machine learning models, but they need to use or purchase relevant models for their tasks
Solution Approach 1:
The patent enables self-service model selection by providing users with an automated system that evaluates multiple candidate models and identifies the most suitable one based on their specific unlabeled dataset. The usefulness score calculation and model ranking are performed automatically without requiring user expertise in model evaluation or training.
3Adaptability or versatility
If multiple sellers provide trained models, then buyers have more choices, but buyers face difficulty in selecting the best model for their particular task
Solution Approach 1:
The patent transforms the complex multi-dimensional model selection problem into a single parameter evaluation - the usefulness score. By changing the selection criterion from multiple complex factors to a unified score based on agreement among candidate models, the system simplifies the selection process while maintaining adaptability to different datasets and tasks.
Data Source
AI summary
Methods and systems for selecting a target model for an unlabeled dataset of a dataset provider, the target model for generating labels for the unlabeled dataset. The method comprises acquiring the unlabeled dataset from the dataset provider; acquiring a first candidate model from a first model provider and a second candidate model from a second model provider, generating a first usefulness score for the first candidate model and a second usefulness score for the second candidate model using the unlabeled dataset, the first and second usefulness scores being indicative of likelihood that the first and second candidate models generate accurate labels for the unlabeled dataset respectively; selecting the first candidate model as the target model using the first usefulness score and the second usefulness score; and causing generation of the labels from the unlabeled dataset using the target model.