How is machine learning algorithm validation with a limited sample size?

How is machine learning algorithm validation with a limited sample size?

Citation: Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS ONE 14 (11): e0224365. https://doi.org/10.1371/journal.pone.0224365 Editor: Enrique Hernandez-Lemus, Instituto Nacional de Medicina Genomica, MEXICO

Is there a bias in machine learning algorithms?

Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting.

How can I estimate how much data I need to use an algorithm?

Perhaps you can look at studies on problems similar to yours as an estimate for the amount of data that may be required. Similarly, it is common to perform studies on how algorithm performance scales with dataset size. Perhaps such studies can inform you how much data you require to use a specific algorithm.

How to evaluate dataset size for machine learning?

Evaluate Dataset Size vs Model Skill It is common when developing a new machine learning algorithm to demonstrate and even explain the performance of the algorithm in response to the amount of data or problem complexity.

Which is the best method to validate ML model?

The advantage of the random subsampling method is that it can be repeated an indefinite number of times. Bootstrapping is another useful method of ML model validation that can work in different situations like evaluating predictive model performance, ensemble methods, or estimation of bias and variance of the model.

How does k-fold cross validation work with small sample sizes?

Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size.

How are validation methods used in machine learning?

We then introduce the different validation methods in section Validation strategies. Our analysis methods are given in section Methods. We have used five clearly defined validation approaches and systematically varied sample size.