Adaptive ML Training Data Updates for Model Accuracy Gaps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning (ML) model training methods face inefficiencies in achieving desired accuracy levels due to unsatisfactory performance on both existing and new features, necessitating improvements in training data management and model adaptation.
Innovation Solution
The system employs a data generation component to identify patterns in input data, generate training and testing datasets, and dynamically update training data based on model performance, ensuring optimal dataset composition for improved model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using existing training datasets, then the models can process input data and produce output data, but the models achieve unsatisfactory accuracy levels due to insufficient or suboptimal training data composition
Solution Approach 1:
The system implements a feedback loop where model performance on test data is evaluated and used to dynamically update training datasets. The accuracy measurement component assesses model performance, and this feedback drives the data generation component to create targeted training data that addresses performance gaps, thereby continuously improving model accuracy through iterative refinement of training data composition
Solution Approach 2:
The system changes parameters of the training data by dynamically adjusting dataset composition based on model performance metrics. The data generation component modifies training data parameters (such as data distribution, sample weights, and feature representations) to optimize model accuracy, transforming static training datasets into adaptive, performance-driven data structures
2Measurement precision
If training data is manually curated and updated, then data quality can be maintained, but the process is time-consuming and computationally expensive
Solution Approach 1:
The system implements self-service through automated data generation and curation. The data generation component automatically creates training data based on patterns learned from existing data and model performance feedback, eliminating the need for manual data curators. The system self-updates its training datasets through automated evaluation and regeneration cycles, significantly reducing time and computational resources required for data maintenance
Solution Approach 2:
The system performs preliminary actions by pre-generating and pre-processing training data in optimized formats. The data generation component prepares training datasets in advance based on predicted model needs and performance requirements, so that when model updates are needed, the data is already structured and ready for immediate use, reducing overall data update time
3Measurement precision
If more training data is added to improve accuracy, then model performance increases, but computational resources and training time increase
Solution Approach 1:
The system applies local quality by generating training data with targeted focus on specific patterns and performance gaps rather than uniformly expanding the entire dataset. The data generation component identifies which data regions need improvement based on model performance analysis and concentrates computational resources on generating only the necessary localized training data, improving accuracy efficiently without proportionally increasing overall computational burden
Data Source
AI summary
The present disclosure provide techniques for automated machine learning (ML) training based on updating training data based on model performance. A system can determine a sample pattern from input data to be used for training a model. The system determines a training dataset and a test dataset including multiple samples corresponding to the sample pattern. After training a model using the training dataset, the system tests the model using the test dataset. The system determines that the trained model's performance with respect to a sample pattern is unsatisfactory, and retrains the model using an updated training dataset that includes additional samples corresponding to the sample pattern.