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

Distributed Collection, Local Intelligence - Stop LLMs from hallucinating with Kong in the Loop architecture

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

CCCE: Adversarial Validation Framework for Quantum Circuit Optimization

Builds a fraud detection system on IEEE-CIS data that explicitly models temporal distribution shift by training adversarial validators to detect when the production distribution diverges from training data, then dynamically reweights ensemble members (LightGBM, CatBoost, XGBoost) based on their robustness to detected drift regimes.