Linear Model Validation via Binary Classifier
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Solution Overview
Problem
Existing methods for validating linear models, such as statistical tests and human expert visual inspection, are inadequate and subjective, leading to unreliable model validation and potential inaccuracies in applications like agriculture and quality control.
Innovation Solution
A system utilizing a binary classifier, trained on data-driven models like convolutional neural networks, to determine whether residual data fulfills homoscedasticity and normality conditions, providing an objective and accurate validation of linear models without relying on fixed rules or human expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical tests or human expert visual inspection are used for validating linear models, then the validation process is simple and easy to implement, but the reliability and objectivity of the validation results are insufficient
Solution Approach 1:
The patent replaces traditional statistical tests and human expert visual inspection with a machine learning-based binary classifier system. This substitution transforms the validation process from subjective mechanical methods to an automated intelligent system that analyzes residual data patterns, thereby improving reliability while managing complexity through algorithmic automation.
Solution Approach 2:
The patent introduces a binary classifier as an intermediary between the residual data and the validation conclusion. This intermediary component processes the residual data through learned patterns and provides an objective decision, bridging the gap between raw data and validation results while enhancing objectivity and reliability.
2Measurement precision
If traditional statistical tests are used for validation, then the method is simple, but subjective biases and inaccuracies occur
Solution Approach 1:
The patent replaces traditional statistical tests with a machine learning-based binary classifier system. This substitution transforms the validation process from subjective mechanical methods to an automated intelligent system that analyzes residual data patterns, thereby improving reliability while managing complexity through algorithmic automation.
Solution Approach 2:
The patent changes the fundamental parameters of the validation approach by transitioning from fixed statistical thresholds to dynamic, data-driven decision boundaries learned by the binary classifier. This parameter transformation enables more precise and adaptive validation that adapts to different data characteristics.
3Reliability
If human expert visual inspection is used, then flexibility in interpretation is available, but objectivity and consistency are compromised
Solution Approach 1:
The patent replaces traditional statistical tests and human expert visual inspection with a machine learning-based binary classifier system. This substitution transforms the validation process from subjective mechanical methods to an automated intelligent system that analyzes residual data patterns, thereby improving reliability while managing complexity through algorithmic automation.
Solution Approach 2:
The patent enables the validation system to perform self-service by automatically analyzing residual data and making validation decisions without requiring human expert intervention. The binary classifier independently processes the data and provides objective results, achieving both automation and consistency.
Data Source
AI summary
The present invention relates to validating a linear model. Input data is received (102) and the linear model to be validated is provided (104). Predicted data is determined based on processing input data by the linear model (106). Residual data is determined based on a difference between the predicted data and the input data (108). A set of validation data including homoscedasticity validation data or normality validation data is generated based on the residual data (110). A binary classifier is provided and used for determining whether the set of validation data fulfills a validation condition (112), namely a homoscedasticity condition or a normality condition. The binary classifier is a trained data driven model that outputs that the validation condition is fulfilled or not fulfilled depending on the set of validation data. Finally, it is determined whether the linear model is valid based on the output of the binary classifier (114).