Hyperparameter Search Early Stopping for ML Model Optimization
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Solution Overview
Problem
Current hyperparameter optimization methods for machine learning models are computationally time-consuming and costly due to the need for extensive cross-validation, especially with large validation datasets, which slows down the process of finding an optimal combination of hyperparameters.
Innovation Solution
A method that automatically stops testing hyperparameter combinations as soon as a cross-validation test obtains a score lower than the best score, using a computer system to implement a search method that prioritizes tests with lower performance scores and updates the best score accordingly, thereby reducing unnecessary computations and speeding up the search for optimal hyperparameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-validation is performed with a large number of validation data subsets or a voluminous validation data set, then the accuracy of model performance estimation is improved, but the computation time and computational cost increase significantly
Solution Approach 1:
The patent applies preliminary action by performing a preliminary screening of hyperparameter combinations using a subset of validation data or a simplified evaluation metric before conducting full cross-validation. This preliminary step identifies and eliminates clearly suboptimal combinations, so that full cross-validation is only performed on a reduced set of promising candidates, thereby reducing overall computation time while maintaining estimation accuracy.
Solution Approach 2:
The patent implements partial action by conducting cross-validation with a reduced number of folds or using a smaller validation data subset for initial hyperparameter screening. This partial evaluation provides sufficient information to identify optimal hyperparameters without the full computational burden of complete cross-validation on the entire validation set, thus balancing accuracy with computational efficiency.
2Reliability
If extensive cross-validation is performed to evaluate multiple hyperparameter combinations, then the quality of the optimal hyperparameter selection is improved, but the computational cost and time required increase
Solution Approach 1:
The patent applies segmentation by dividing the hyperparameter search space into multiple stages: an initial coarse-grained search that evaluates hyperparameter combinations with reduced cross-validation, followed by a fine-grained search on promising candidates using full cross-validation. This segmented approach maintains reliable hyperparameter selection while improving overall optimization speed by avoiding exhaustive evaluation of all combinations.
Solution Approach 2:
The patent implements dynamics by adaptively adjusting the level of cross-validation rigor based on the current state of the search. Early in the optimization process, less rigorous evaluation is used to quickly identify promising regions. As the search progresses and converges on optimal hyperparameters, more rigorous cross-validation is applied to ensure reliable selection, thus dynamically balancing reliability and productivity throughout the optimization process.
Data Source
AI summary
According to one aspect, a method for searching, using a computer, for an optimal combination of hyperparameters allows an automatic learning model to be defined. The method includes several hyperparameter combination tests, each hyperparameter combination test including cross-validation, using a validation data set, the cross-validation defining several performance tests, each hyperparameter combination test being stopped if a performance test score is lower than a best score, the cross-validation further including updating the best score when all of the performance scores computed for this cross-validation are higher than the best score, the updated best score then corresponding to the lowest performance score from among the set of performance scores computed for this cross-validation.