This repo is for testing machine learning models on small (classification) datasets. There is a blog post that summarizes the experiments here: https://www.data-cowboys.com/blog/which-models-are-best-for-small-datasets

The relevant figures are produced in figures.ipynb. The actual experimental set-up is in files that start with 01_*, 02_*, etc. Nested cross-validation is used to get unbiased estimates of generalization performance. The splits are stratified random with fixed seeds, so the conclusions of these experiments are unlikely to hold for "real" data where test/production data is not IID with the training data.

All that said, here are some observations:

• Non-linear models are better than linear ones, even for datasets with < 100 samples.
• SVM and Logistic Regression do similarly, but there are two datasets where SVM is the only algorithm that does not fail catastrophically. However, logistic regression with elasticnet penalty never gets less than 0.5 area under the ROC curve.
• LightGBM works well. Giving it more hyperparameters to try is a good idea. The hyperopt package did better than scikit-optimize (not shown) and Optuna (not shown) and hpbandster-sklearn (see open PR), but any of those could be user error.
• AutoGluon amd mljar work really well and are the best approaches for predictive power. But you need to give them enough time. A 2m budget (per fold) was not enough, but 5m was enough for datasets up to 10k samples.

The data is subset of this dataset-of-datasets: "UCI++, a huge collection of preprocessed datasets for supervised classification problems in ARFF format "

Note that UCI++ reuses the same datasets in different configurations and often you can't tell what's a categorical feature.