A full pipeline AutoML tool for tabular data

A tiny framework to perform adversarial validation of your training and test data.

Use patient health data from MIT's GOSSIS(Global Open Source Severity of Illness Score) to do an experiment, in which we want to evaluate the question of which modeling strategy leads to the most effective predictions.

Train CatBoost & XGBoost on 59K data to predict the probability that an online transaction is fraudulent

Code for article https://ilias-ant.github.io/blog/adversarial-validation/.