Data Slice Mining for Scalable ML Edge Case Detection
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Solution Overview
Problem
Existing data slice finding methods for machine learning models are not scalable, do not provide a nuanced understanding of error types, and result in a large number of data slices that are difficult to analyze, hindering effective model validation and improvement.
Innovation Solution
The use of Frequent Pattern Mining combined with heuristics for data slice finding, which includes attribute length constraints and error-specific slice identification, provides efficient and granular analysis of model errors, reducing processing time and enhancing understanding of model limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data slice finding methods are used, then model evaluation can be conducted, but the methods are not scalable when dealing with many metadata features
Solution Approach 1:
The patent segments the data slice finding process into two distinct phases: (1) frequent pattern mining to identify commonly occurring feature value combinations, and (2) slice generation based on these patterns. This segmentation allows the system to handle large numbers of metadata features efficiently by first reducing the search space through pattern identification, then generating slices only from meaningful patterns rather than exhaustively searching all possible feature combinations.
Solution Approach 2:
The patent performs preliminary action by conducting frequent pattern mining before generating data slices. This preliminary step identifies which feature value combinations occur frequently in the dataset, allowing the system to focus subsequent slice generation on these meaningful patterns. This pre-processing step significantly reduces the computational complexity of the overall process when dealing with many metadata features.
2Loss of information
If traditional data slice finding methods are used, then data slices can be generated, but they do not provide a nuanced understanding of different error types
Solution Approach 1:
The patent applies local quality by generating data slices that are specific to different error types rather than creating a single aggregated slice. The system identifies slices for false positives, false negatives, and other error categories separately, allowing analysts to examine the characteristics and patterns of each error type independently. This provides nuanced information about model failures without requiring complex manual analysis to differentiate error sources.
Solution Approach 2:
The patent performs partial action by allowing users to select which error types to analyze rather than requiring comprehensive analysis of all possible error categories. Users can focus on specific error types relevant to their needs, reducing analysis complexity while still gaining meaningful insights into model performance and limitations.
3Loss of information
If traditional data slice finding methods are used, then data slices can be identified, but they result in a large number of slices making it difficult for experts to read and understand
Solution Approach 1:
The patent extracts only the most relevant and meaningful data slices by using frequent pattern mining to identify commonly occurring feature value combinations. Instead of generating all possible slices, the system extracts slices based on patterns that actually appear in the data, filtering out rare or meaningless combinations. This reduces the number of slices presented to analysts while maintaining comprehensive error analysis coverage.
Solution Approach 2:
The patent changes parameters by introducing constraints on the number and characteristics of generated slices. The system adjusts slice generation parameters to produce a manageable number of high-quality slices rather than an exhaustive list. This allows experts to review and understand the slices effectively while still obtaining comprehensive error analysis information.
Data Source
AI summary
Methods for a machine-learning network that provide efficient, scalable, and granular analyses during validation of a machine learning model are disclosed. Validation of models depends upon many factors, including the real-world application of the model, the type of model being trained, and the types of data samples it is being trained on. In order to provide relevant edge case information to users that pertains to their specific model, data slice finding techniques may be used to identify subsets of the dataset that are particularly problematic. By limiting a length of the slice description that the algorithm searches and by configuring the algorithm to target specific types of errors, users are provided with a more granular analysis that then allows them to determine how or if they need to retrain the model.