Jayita Gulati compares a few mechanisms to classify data:
When you have a small dataset, choosing the right machine learning model can make a big difference. Three popular options are logistic regression, support vector machines (SVMs), and random forests. Each one has its strengths and weaknesses. Logistic regression is easy to understand and quick to train, SVMs are great for finding clear decision boundaries, and random forests are good at handling complex patterns, but the best choice often depends on the size and nature of your data.
In this article, we’ll compare these three methods and see which one tends to work best for smaller datasets.
All three are quite reasonable algorithms to compare, though I’d want to add in gradient descent or XGBoost, as I’d expect it to perform better than random forest with small datasets.