Predictive Model Accuracy Evaluation via Reduced Complexity Analysis
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Solution Overview
Problem
Existing predictive computational models face challenges in automatically determining their accuracy, leading to potential costly mispredictions and inefficient resource allocation due to the complexity of tracking multiple evaluation metrics and thresholds.
Innovation Solution
A method that involves using a trained reduced complexity model to evaluate the accuracy of a predictive computational model by selecting metrics, determining their type, and generating thresholds, thereby automatically assessing the model's quality and triggering re-training or re-deployment as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of predictive model accuracy is performed using multiple metrics and thresholds, then measurement precision is improved, but device complexity and loss of time increase
Solution Approach 1:
The system performs self-evaluation by automatically comparing its own predictive model outputs against established metrics and thresholds, eliminating the need for external manual evaluation and reducing complexity of the evaluation infrastructure
Solution Approach 2:
The evaluation system uses a universal set of metrics and thresholds that can assess multiple types of predictive models across different domains, simplifying the evaluation process by providing a standardized multi-functional assessment framework
2Measurement precision
If manual evaluation of predictive model accuracy is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
Metrics and thresholds are pre-established and stored in the system before actual model evaluation occurs, allowing for rapid automated comparison and assessment without time-consuming manual setup during the evaluation process
Solution Approach 2:
The manual mechanical process of evaluating model accuracy is replaced with an automated computational system that programmatically compares model outputs against predefined metrics, dramatically reducing evaluation time while maintaining precision
3Reliability
If predictive model complexity is increased to improve prediction accuracy, then reliability is improved, but device complexity and computational cost increase
Solution Approach 1:
The system evaluates multiple models with different parameter configurations and complexity levels, automatically selecting the model that achieves the required prediction accuracy with optimal complexity, thereby improving reliability without unnecessary complexity increases
Data Source
AI summary
A processor may acquire a trained predictive computational model from a database. The processor may apply a trained reduced complexity model to the trained predictive computational model. The trained reduced complexity model may be associated with the trained predictive computational model. The processor may select at least one metric. The processor may determine a quality indicator related to the at least one metric by identifying the type of the at least one metric, evaluating the output of the trained predictive computational model in relation to the type of the at least one metric, and generating, based on the evaluation of the trained predictive computational model, a threshold associated with the at least one metric. The processor may determine the accuracy of the trained predictive computational model based on the quality indicator.