This repository contains the official implementation (PyTorch) of "Multimodal Forgery Detection Using Ensemble Learning" proposed in APSIPA Paper 2022.

This project uses a synthetic healthcare dataset to predict patient test results ("Normal," "Abnormal," or "Inconclusive") using machine learning. It employs ensemble methods such as Bagging, Random Forest, Boosting, and Stacking classifiers with feature engineering and preprocessing.

A powerful ensemble learning class 💪🤖 that supports multi-layer stacking 📚 and blending models 🔄 for regression tasks 📉, with K-fold cross-validation 🔄✅ and hold-out validation set options 🛑, for robust model performance 🚀.