ML Underperformance Identification via Feature Segmentation
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Solution Overview
Problem
Machine learning systems face challenges in identifying areas where they underperform, even if their overall performance is acceptable, making it difficult for users to pinpoint and address deficiencies.
Innovation Solution
A method and system that create a Cartesian model by dividing case records into groups based on feature values, calculating performance parameters, and identifying characteristics of groups with performance below a threshold, allowing users to pinpoint underperforming areas and improve the ML engine's performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the machine learning system provides an overall acceptable performance parameter, then the system can operate without frequent interventions, but it cannot identify specific areas where it underperforms
Solution Approach 1:
The patent segments the case collection into multiple groups based on feature values, allowing the system to analyze performance in specific segments rather than only overall. This enables identification of underperforming areas while maintaining overall system operation.
Solution Approach 2:
The patent introduces a new dimension of analysis by creating a Cartesian model that combines multiple feature values to define specific areas. This transforms the performance analysis from a single overall metric to a multi-dimensional space where underperforming regions can be identified and targeted.
2Measurement precision
If the system analyzes all case records in detail to identify underperforming areas, then comprehensive performance insights are obtained, but the complexity and computational resources increase
Solution Approach 1:
By dividing the case collection into groups based on feature values, the system reduces the complexity of analyzing all cases individually. Each group can be analyzed separately, making the overall process more manageable while maintaining comprehensive coverage.
Solution Approach 2:
The patent applies local quality analysis by examining performance characteristics specific to each group defined by feature combinations. This allows precise identification of underperforming areas without requiring equally detailed analysis of all cases, optimizing the balance between precision and complexity.
3Loss of information
If the system divides case records into multiple groups based on feature values, then specific underperforming areas can be identified, but the model complexity increases
Solution Approach 1:
The Cartesian model serves multiple functions: it organizes case records into groups, identifies underperforming areas, and provides a framework for targeted analysis. This multi-functionality reduces the need for separate complex models for each task.
Solution Approach 2:
The system performs preliminary grouping of case records based on feature values before detailed performance analysis. This pre-organization simplifies subsequent analysis by reducing the search space and enabling more efficient identification of underperforming areas.
Data Source
AI summary
A method, system and computer program product, the method comprising: creating a model representing underperforming cases; from a case collection having a total performance, and which comprises for each of a multiplicity of records: a value for each feature from a collection of features, a ground truth label and a prediction of a machine learning (ML) engine, obtaining one or more features; dividing the records into groups, based on values of the features in each record; for one group of the groups, calculating a performance parameter of the ML engine over the portion of the records associated with the group; subject to the performance parameter of the group being below the total performance in at least a predetermined threshold: determining a characteristic for the group; adding the characteristic of the group to the model; and providing the model to a user, thus indicating under-performing parts of the test collection.