Analytical Model Accuracy Evaluation via Perturbation Response
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Solution Overview
Problem
There is a need for an efficient, differentiable, and intuitive measure to predict the generalization of trained analytical models and their response to perturbations in data, as existing methods lack a comprehensive framework for evaluating model accuracy and invariance to transformations.
Innovation Solution
A framework is developed that evaluates analytical models by perturbing training data and generating perturbation response curves, using deviation scores like the Gi-score and Pal-score to compare the model's performance to an idealized model, allowing for the assessment of model invariance and accuracy across varying perturbations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional accuracy measurement methods are used, then model accuracy can be quantized compared to known results, but these methods lack the ability to comprehensively evaluate model invariance to data transformations and perturbations
Solution Approach 1:
The patent creates a universal evaluation framework that simultaneously measures multiple aspects of model performance: traditional accuracy on original data, invariance to various transformations (rotations, translations, scaling), and robustness to perturbations. This multi-functional framework replaces the need for separate evaluation methods with a single comprehensive system that outputs multiple metrics including accuracy scores and invariance scores.
2Measurement precision
If model evaluation is performed on original training data only, then accuracy can be measured, but the model's generalization capability and response to perturbations cannot be predicted
Solution Approach 1:
The patent applies preliminary actions by systematically transforming the training data before model evaluation. Various transformations (rotations, translations, scaling, perturbations) are pre-applied to create multiple versions of the training data, which then serve as test inputs. This preliminary transformation step enables the model's generalization capability to be assessed before actual deployment, predicting how the model will respond to unseen variations in real-world data.
3Adaptability or versatility
If comprehensive perturbation analysis is performed on all possible data transformations, then model invariance can be thoroughly assessed, but the computational complexity and time required increase significantly
Solution Approach 1:
The patent implements partial action by selecting and applying only the most relevant transformations and perturbations to the training data, rather than exhaustively testing all possible variations. The system identifies key transformation types (rotations, translations, scaling) and applies them at selected levels, providing a balanced assessment of model invariance without requiring evaluation of every conceivable data variation. This approach maintains comprehensive invariance assessment while controlling computational time.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to classifying accuracy of analytical model, such as a neural network. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an accessing component that accesses an analytical model, a deviation component that generates combined results of the analytical model in response to a set of inputs that vary in degree of perturbation of a set of test data, and an analysis component that compares a range of the combined results to a range of the ideal results.