ML Data Augmentation Selection Using Robustness Metrics
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Solution Overview
Problem
Existing machine learning models lack effective tools for evaluating robustness and performance, leading to tedious discussions and incomplete reports, and conventional tools fail to provide integrated, automated testing across various applications, hindering deployment in high-risk industries.
Innovation Solution
A system and interface for generating detailed, interactive reports that evaluate model robustness and performance using parallel computing, creating augmented datasets through data transformations, and recommending improvements based on robustness metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data augmentation tools are used, then dataset size is increased, but model robustness evaluation is not automatically tested and computation time increases
Solution Approach 1:
The system performs preliminary evaluation of data augmentations by creating test datasets with transformations applied and evaluating model performance before full deployment. This preliminary testing identifies which augmentations will improve robustness without requiring exhaustive computation of all possible augmentations.
Solution Approach 2:
The system implements automated feedback loops where model performance is continuously evaluated on test datasets generated from various data transformations. The feedback from these evaluations automatically guides the selection and application of augmentations that improve robustness, eliminating manual iteration and reducing overall computation time.
2Loss of information
If extensive scripts are written for deep insight into ML solution, then model understanding is improved, but ease of operation decreases and reports become incomplete
Solution Approach 1:
The system performs self-service by automatically generating comprehensive evaluation reports and insights without requiring extensive manual scripting. The automated system creates test datasets, evaluates model performance, and produces detailed reports that would otherwise require significant manual effort, thereby improving ease of operation while maintaining deep model insight.
Solution Approach 2:
The system provides universal functionality by integrating multiple evaluation tasks into a single automated framework. It simultaneously performs data transformation testing, model evaluation, robustness assessment, and report generation, replacing multiple separate scripting efforts with one multi-functional system that improves both insight and ease of operation.
3Reliability
If conventional evaluation methods are used, then model performance is assessed, but automated robustness testing across multiple test types is not provided
Solution Approach 1:
The system merges multiple evaluation functions into a unified automated testing framework. It combines data transformation applications, model inference, performance scoring, and robustness metric calculation into an integrated system that automatically executes multiple test types, thereby improving robustness evaluation without proportionally increasing system complexity.
Solution Approach 2:
The system automatically varies transformation parameters across multiple test datasets to evaluate model robustness. By systematically changing parameters such as transformation intensity, data type, and test conditions, the system comprehensively assesses robustness without requiring complex manual configuration for each test scenario.
4Loss of information
If detailed reports are generated for model validation, then stakeholder understanding is improved, but productivity decreases due to manual report creation
Solution Approach 1:
The system generates detailed evaluation reports automatically without manual intervention. It self-services by collecting performance data from multiple test datasets, calculating robustness metrics, and producing comprehensive reports that provide detailed evaluation information while significantly improving productivity through automation.
Solution Approach 2:
The system maintains continuous automated evaluation and report generation throughout the model development process. Rather than creating discrete manual reports, the system continuously executes tests and updates evaluations, providing ongoing detailed information that improves both stakeholder understanding and evaluation speed through uninterrupted automated operation.
Data Source
AI summary
Producing an augmented dataset to improve performance of a machine learning model. A test series is created for a first type of data transformation. the test series defining a set of test values for at least one parameter characterizing the first type of data transformation. Test datasets are generated based on a source dataset, each of the test datasets corresponding to a respective test value of the set of test values for said at least one parameter characterizing the first type of data transformation. Each of the test datasets is input to the machine learning model to produce a corresponding model output. At least one score is determined for each test dataset based at least in part on the corresponding model output. Robustness metrics of the first type of data transformation are determined based on a function which maps said at least one score of each of the test datasets to said at least one parameter characterizing the first type of data transformation. A set of one or more data augmentations are determined to be applied to the source dataset based at least in part on said one or more robustness metrics of the first type of data transformation. An augmented dataset is generated based on the source dataset using the determined set of one or more data augmentations.