Prediction Error Factor Identification via Multi-Metric Evaluation
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Solution Overview
Problem
Current metric monitoring systems and prediction model maintenance systems require expert analysis to identify factors of prediction errors, as they individually calculate and present metrics, and do not automatically identify prediction error factors based on evaluation results.
Innovation Solution
An analysis device and method that calculates and evaluates multiple metrics for prediction models, identifying error factors through a combination of evaluation results using a metric evaluation unit and factor identification unit, allowing for automatic identification of prediction error factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple metrics are calculated and evaluated individually, then comprehensive evaluation data is obtained, but expert analysis is still required to identify prediction error factors
Solution Approach 1:
The patent combines multiple individually evaluated metrics into a unified analysis framework. The factor identification unit integrates results from various metric evaluations (prediction accuracy, distribution shift magnitude, etc.) to collectively identify prediction error factors, transforming scattered individual evaluations into a comprehensive automated identification system.
Solution Approach 2:
The patent introduces a factor identification unit as an intermediary between metric evaluation and error factor identification. This intermediary component processes multiple metric evaluation results and translates them into identified prediction error factors, eliminating the need for expert analysis while maintaining comprehensive evaluation capabilities.
2Measurement precision
If manual analysis is performed to identify prediction error factors, then accurate identification is achieved, but time-consuming processes occur
Solution Approach 1:
The system performs self-service by automatically identifying prediction error factors through the factor identification unit. The unit autonomously processes metric evaluation results and identifies error factors without human intervention, achieving both accurate identification and time efficiency through automated self-analysis capabilities.
Solution Approach 2:
The patent replaces the mechanical process of manual expert analysis with an automated computational system. The factor identification unit uses algorithmic processing to substitute human expert analysis, maintaining identification accuracy while dramatically reducing the time required for error factor identification.
3Device complexity
If individual metric evaluation is used, then simple calculation is maintained, but automatic identification of error factors is not achieved
Solution Approach 1:
The factor identification unit serves multiple functions: it receives various metric evaluation results, processes them according to different evaluation criteria, and identifies different types of prediction error factors. This multi-functional design enables automatic identification while maintaining relatively simple system architecture through a single versatile identification unit.
Data Source
AI summary
Provided are an analysis device, an analysis method, and a program capable of easily identifying a factor of a prediction error in prediction using a prediction model on the basis of various viewpoints. An analysis device (1) includes: a metric evaluation unit (2) that calculates and evaluates a plurality of types of metrics with respect to a prediction model, data of explanatory variables used in the prediction model, or data of target variables used in the prediction model; and a factor identification unit (3) that identifies a factor of an error in prediction by the prediction model according to a combination of evaluation results of the plurality of types of metrics.