Computer-Based Model Optimization Across Classification Cutoff Values
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Solution Overview
Problem
Existing computer-based models lack comprehensive evaluation and modification processes, leading to reduced accuracy in data classification due to reliance on specific configurations without considering a broad range of cutoff values, resulting in suboptimal performance.
Innovation Solution
A model development system iteratively evaluates, re-configures, and re-trains computer-based models using partial area under the curve techniques to determine optimal configurations by adjusting input parameters, hyper-parameters, and weights, focusing on relevant cutoff value ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the computer-based model is trained using specific configuration (specific set of input parameters, cutoff value), then the model can learn patterns from historical data, but the improvements are not based on comprehensive evaluation of model performance, reducing accuracy
Solution Approach 1:
The patent implements dynamic evaluation by iteratively adjusting cutoff values and re-evaluating model performance across multiple configurations. The system dynamically modifies the evaluation process to consider various cutoff ranges rather than relying on a single fixed configuration, enabling comprehensive performance assessment that adapts to different operational scenarios.
Solution Approach 2:
The patent changes the evaluation parameters by introducing multiple cutoff values and ranges for comprehensive performance measurement. Instead of using a single cutoff value, the system evaluates model performance across different parameter settings, allowing the model to be optimized for various operational conditions and improving overall accuracy and adaptability.
2Productivity
If the computer-based model uses a single cutoff value for classification, then the classification process is simple and fast, but the model performance is suboptimal because it does not consider a broad range of cutoff values
Solution Approach 1:
The patent performs preliminary evaluation across multiple cutoff values and configurations before final model selection. By pre-evaluating the model's performance across different cutoff ranges and identifying optimal configurations in advance, the system ensures that the selected model achieves high accuracy while maintaining efficient classification speed.
Solution Approach 2:
The patent implements feedback mechanisms where model performance is continuously evaluated and used to adjust future training and evaluation processes. The system uses performance feedback from multiple cutoff value evaluations to refine the model, ensuring that the final configuration achieves optimal balance between accuracy and processing speed.
3Measurement precision
If the model is re-trained multiple times with different configurations, then the comprehensive evaluation and modification process improves accuracy, but the training time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing evaluation and re-training efforts on the most relevant cutoff ranges and configurations rather than exhaustively testing all possible settings. The system identifies and prioritizes critical evaluation areas, achieving comprehensive performance assessment without requiring complete exploration of all parameter spaces, thus reducing unnecessary training time.
Solution Approach 2:
The patent maintains continuous evaluation and optimization cycles where each re-training iteration builds upon previous findings. The systematic continuation of evaluation across multiple configurations ensures that each training cycle contributes meaningfully to improving accuracy, avoiding redundant computations by leveraging insights from prior evaluations.
Data Source
AI summary
Methods and systems are presented for a computer-based models enhancing by iteratively evaluating and modifying a computer-based model configured to perform data classifications. A model development system builds a computer-based model under an initial configuration. The model development system may evaluate the performance of the computer-based model under the initial configuration using a partial area under the curve technique. The model development system may re-configure the computer-based model under a different configuration. The different configuration may specify a different number of input parameters for the computer-based model, different input parameters, different hyper-parameters, and/or different weights assigned to the training data. The model development system may continue to re-configure the computer-based models under different configurations and evaluate the computer-based models under the different configurations to determine an optimal configuration for deploying the computer-based model.