Tabular data is the most prevalent form of structured data, necessitating robust models for classification and regression tasks. Traditional models like eXtreme Gradient Boosting (XGBoost) have gained ...

This gap in deep learning for tabular data led to exploring alternative, more efficient architectures. Researchers from Yandex and HSE University introduced a model named TabM, built upon an MLP ...

In solving real-world data science problems, model selection is crucial. Tree ensemble models like XGBoost are traditionally favored for classification and regression for tabular data. Despite their ...

📜 arXiv 📦 Python package 📚 Other tabular DL projects This is the official implementation of the paper "Revisiting Deep Learning Models for Tabular Data".

Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning ...