Dataset Management for Machine Learning Model Training
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Solution Overview
Problem
Machine learning model training requires significant processing resources and is often hindered by the scarcity or poor quality of training data, making efficient dataset management crucial for improving model performance.
Innovation Solution
A method that trains machine learning models on increasing-sized sets of training samples, generates class-specific performance metrics, and extrapolates learning curves to predict performance improvements, optimizing augmentation actions for dataset augmentation to enhance model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training data size is increased to improve model performance, then model accuracy is improved, but processing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by generating learning curves and extrapolating performance metrics before actually training the model on large datasets. This allows prediction of optimal training configurations and data requirements in advance, avoiding unnecessary resource consumption during actual training while still achieving high model accuracy.
Solution Approach 2:
The system uses its own training results to generate learning curves and automatically extrapolates performance metrics to determine optimal training parameters. This self-service capability eliminates the need for external trial-and-error experimentation, reducing processing resources while maintaining model accuracy optimization.
2Measurement precision
If comprehensive dataset augmentation is performed to improve model performance across all classes, then overall model accuracy is improved, but time and resource consumption increases
Solution Approach 1:
Instead of uniformly augmenting all classes, the system generates class-specific learning curves and identifies individual classes that benefit most from additional training data. This localized approach focuses resources on specific classes with suboptimal performance, improving overall model accuracy while reducing total training time compared to comprehensive augmentation of all classes.
Solution Approach 2:
The system changes parameters by extrapolating performance metrics to predict the optimal amount of additional training data needed for each class. This allows precise control over data augmentation parameters, avoiding excessive time consumption while achieving the necessary improvement in model accuracy for each specific class.
3Measurement precision
If learning curves are generated for all classes to optimize data distribution, then model performance optimization is improved, but computational overhead increases
Solution Approach 1:
The system extracts only the essential information needed for optimization by generating learning curves and extrapolating metrics specifically for data distribution decisions. Rather than performing comprehensive analysis of all model parameters, it focuses extraction on the specific metrics relevant to training data allocation, reducing computational overhead while maintaining effective model performance optimization.
Data Source
AI summary
A method, a computer system, and a computer program product for managing a dataset of training samples, labeled by class, during training of a machine learning model is provided. Embodiments of the present invention may include training the model on a sequence of increasing-sized sets of the training samples and testing performance of the model after training with each set to obtain class-specific performance metrics corresponding to each set size. Embodiments of the present invention may include generating class-specific learning curves from the performance metrics for the plurality of classes. Embodiments of the present invention may include extrapolating the learning curves. Embodiments of the present invention may include optimizing a function of the predicted performance metrics to identify a set of augmentation actions to augment the dataset for further training of the model. Embodiments of the present invention may include providing an output indicative of the set of augmentation actions.