Test Data Overutilization Validation for ML Model Reliability
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Solution Overview
Problem
Existing methods for validating test data sets for computer-based machine learning modules are inadequate, leading to potential overfitting and decreased performance when new data sets are encountered.
Innovation Solution
A method is introduced to validate test data sets by calculating a 'degree of overutilization' using a second unseen test data set. This degree characterizes the suitability of the first test data set for further evaluations, allowing for classification as suitable or not suitable based on predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the same test data set is used multiple times to evaluate the model, then the model performance on training data improves, but the model suffers from overfitting and deteriorates on new unseen data sets
Solution Approach 1:
The patent applies preliminary action by introducing a validation step before reusing test data. The degree of overutilization is calculated in advance using a second validation data set to predict whether reusing the first test data set will cause overfitting. This preliminary assessment prevents harmful reuse while allowing beneficial reuse, resolving the contradiction between improving model performance and maintaining reliability on new data.
2Reliability
If new test data sets are collected to avoid overfitting, then model reliability on new data improves, but the time and resources required for data collection and validation increase
Solution Approach 1:
The patent uses copying by creating a second validation data set that serves as a proxy for future unseen data. Instead of collecting multiple new test data sets, the system copies the validation approach using a single additional data set to assess the risk of overutilization. This reduces the time and resources needed while maintaining reliability assessment.
3Measurement precision
If the degree of overutilization is calculated using a second validation data set, then the accuracy of overfitting prediction improves, but the complexity of the validation process increases
Solution Approach 1:
The patent applies segmentation by dividing the validation process into two independent parts: a first test data set for model evaluation and a second validation data set for overutilization assessment. This segmentation allows each data set to serve a specific purpose, improving measurement precision while keeping the overall process manageable through clear separation of functions.
Data Source
AI summary
A method for validating a test data set for a computer-based machine learning module that contains at least one model. The method includes receiving first and second test data sets for the at least one model; calculating a degree of overutilization of the first test data set using the second test data set, wherein the degree of overutilization characterizes whether the first test data set is useful for evaluating the performance of the at least one model; classifying the first test data set as suitable for the subsequent evaluation of the at least one model if the degree of overutilization of the first test data set satisfies a predefined data set criterion, and otherwise, classifying the first test data set as not suitable for the subsequent evaluation of the at least one model.