Hyperparameter Optimization for Machine Learning Ensemble Accuracy
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Solution Overview
Problem
Training machine learning ensembles requires significant computing resources and time, and using the same hyperparameters for all models lowers the overall accuracy of the ensemble.
Innovation Solution
Diverse hyperparameters are used to train multiple base models, which are then processed to generate a machine learning ensemble using a meta learner that assigns weights based on prediction accuracy, reducing training time and improving ensemble accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple base models are trained individually to form an ensemble, then the accuracy of the ensemble is improved, but the computing resources and training time required increase significantly
Solution Approach 1:
The system pre-trains multiple base models with diverse hyperparameters before forming the ensemble. By preparing the model diversity in advance through hyperparameter variation, the system achieves high ensemble accuracy without requiring excessive training time during the actual ensemble formation process.
Solution Approach 2:
The system varies hyperparameters (such as learning rate, batch size, regularization strength) across different base models to create diversity. This parameter change approach allows the ensemble to capture different aspects of the data distribution, improving accuracy while maintaining efficient training through shared architectural components.
2Ease of manufacture
If the same hyperparameters are used for all base models in the ensemble, then the training process is simplified, but the overall accuracy of the ensemble is reduced
Solution Approach 1:
Different base models in the ensemble are trained with different hyperparameter settings tailored to their specific roles and characteristics. This local quality approach allows each model to optimize for particular aspects of the learning task, improving overall ensemble accuracy while maintaining a systematic framework for hyperparameter management.
Solution Approach 2:
The system introduces asymmetric hyperparameter configurations across base models rather than using uniform settings. By deliberately creating asymmetry in hyperparameter values (e.g., varying learning rates, depths, or regularization parameters), the ensemble achieves better representation of complex data patterns while maintaining manageable training through automated hyperparameter optimization.
Data Source
AI summary
One or more computing devices, systems, and/or methods for hyper parameter optimization for machine learning ensemble generation are provided. For example, one or more base models are trained using diverse sets of hyper parameters, wherein different sets of hyper parameters (e.g., hyper parameters with different values) are used to train different base models. A matrix, populated with predictions from the set of base models, is generated. A machine learning ensemble is generated by processing the matrix utilizing a meta learner.