Training Data Estimation Function for ML Model Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models often require significant amounts of training data to achieve target validation performance, leading to costly and time-consuming data collection and potential overtraining, while underestimating data needs can result in additional computational overhead and workflow delays.
Innovation Solution
The development of a training data collection estimation function that uses concave monotonic increasing regression functions to estimate the required number of additional training samples needed to meet a target validation score, minimizing resource usage and avoiding overtraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more training data is collected to increase validation performance, then model accuracy improves, but data collection time and cost increase significantly
Solution Approach 1:
The system performs preliminary action by training the model with an initial training data set before full-scale data collection, enabling early validation performance assessment and preventing unnecessary extensive data collection
Solution Approach 2:
The system implements feedback by computing validation scores after initial training and using these scores to determine whether additional data collection is needed, creating a closed-loop decision process that prevents盲目 data collection
2Reliability
If more training data is collected to increase validation performance, then model accuracy improves, but annotation cost increases significantly
Solution Approach 1:
The system performs preliminary action by training with an initial training data set before extensive data collection, enabling early validation performance assessment and preventing unnecessary extensive data collection
Solution Approach 2:
The system implements feedback by computing validation scores after initial training and using these scores to determine whether additional data collection is needed, creating a closed-loop decision process
3Reliability
If more computing resources are allocated for data processing and model training, then validation performance improves, but resource waste increases due to overtraining
Solution Approach 1:
The system performs preliminary action by training with an initial training data set before extensive data collection, enabling early validation performance assessment and preventing unnecessary extensive data collection
Solution Approach 2:
The system implements feedback by computing validation scores after initial training and using these scores to determine whether additional data collection is needed, creating a closed-loop decision process
4Quantity of substance
If additional data collection is underestimated to save resources, then resource usage decreases, but workflow delays increase due to needing more data later
Solution Approach 1:
The system performs preliminary action by training with an initial training data set before extensive data collection, enabling early validation performance assessment and preventing unnecessary extensive data collection
Solution Approach 2:
The system implements feedback by computing validation scores after initial training and using these scores to determine whether additional data collection is needed, creating a closed-loop decision process
Data Source
AI summary
Approaches for training data set size estimation for machine learning model systems and applications are described. Examples include a machine learning model training system that estimates target data requirements for training a machine learning model, given an approximate relationship between training data set size and model performance using one or more validation score estimation functions. To derive a validation score estimation function, a regression data set is generated from training data, and subsets of the regression data set are used to train the machine learning model. A validation score is computed for the subsets and used to compute regression function parameters to curve fit the selected regression function to the training data set. The validation score estimation function is then solved for and provides an output of an estimate of the number additional training samples needed for the validation score estimation function to meet or exceed a target validation score.