Machine Learning Model Evaluation via Deterioration Index
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Solution Overview
Problem
Existing methods for evaluating machine learning models primarily focus on overall performance compatibility, lacking the ability to assess performance across multiple groups or viewpoints.
Innovation Solution
An information processing device that acquires conditions for evaluation data, calculates performance indices for a machine learning model before and after updates using specified data sets, and computes a deterioration index to evaluate performance changes across various groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If overall performance evaluation is used, then evaluation simplicity is improved, but evaluation comprehensiveness deteriorates
Solution Approach 1:
The patent segments the evaluation process into multiple dimensions by introducing condition-specific performance indexes. Instead of a single overall evaluation, the system divides evaluation into multiple conditions (e.g., different data sets, scenarios, or operational contexts), calculating performance indexes for each condition separately. This segmentation allows comprehensive evaluation across multiple aspects while maintaining systematic simplicity through structured aggregation of condition-specific results.
2Loss of information
If multiple groups are considered in evaluation, then evaluation comprehensiveness is improved, but evaluation complexity deteriorates
Solution Approach 1:
The patent implements a universal evaluation framework that handles multiple groups and conditions through a unified approach. The system uses a standardized performance index calculation method that can be applied across different conditions and groups, with a single deterioration index formula that aggregates results from multiple sources. This multi-functional framework comprehensively evaluates multiple groups while avoiding the complexity of separate evaluation systems for each group.
Data Source
AI summary
In order to provide a model evaluation method in which a plurality of groups are considered, an information processing device according to the present invention includes a data acquisition means that acquires at least one condition for evaluation data of a machine learning model, a performance calculation means that calculates a performance index of the machine learning model and a performance index of the machine learning model after being updated using a data set specified for each of the at least one condition, and an index calculation means that calculates a deterioration index of a performance of the machine learning model based on the performance indexes before and after the machine learning model is updated.