What is minimum size of dataset required for ML model?
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The minimum size of the dataset required for training machine learning models varies depending on the specific task and model being used. In the context of Software Engineering, where small and medium-sized datasets are common, pre-trained Transformer models have shown effectiveness even on small datasets, with some tasks requiring less than 1,000 samples . For clinical validation studies of machine learning models, Sample Size Analysis for Machine Learning (SSAML) provides a standardized approach to estimating sample sizes, ensuring precision and accuracy at a desired confidence level, with minimum sample sizes determined based on standardized criteria . Therefore, the minimum dataset size needed for training a machine learning model can be influenced by factors such as the complexity of the task, the model architecture, and the desired level of performance.
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| Papers (5) | Insight |
|---|---|
| Minimum dataset sizes vary based on the specific ML model and study criteria. In the discussed validation studies, sample sizes ranged from 40 patients to 1500 patients, depending on the model. | |
| The paper focuses on minimizing fill distance in training sets for regression models, not specifying a minimum dataset size requirement for machine learning models. | |
| The minimum dataset size required for ML model validation studies can be determined using the open-source method SSAML, ensuring precision and accuracy at a desired confidence level. | |
| For small Software Engineering datasets, modern machine learning models like pre-trained Transformers can be effective, even with less than 5,000 samples. | |
Open access•Posted Content | The paper suggests that modern machine learning techniques, like pre-trained Transformers, can be effective on small datasets (< 1,000 samples) in Software Engineering tasks. |
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