In their paper, Tabular Data: Deep Learning is Not All You Need, the authors argue that while deep learning methods have shown tremendous success in the image and text domains, traditional tree-based methods like XGBoost still continue to shine when it comes to tabular data. The authors examined Tabnet, Neural Oblivious Decision Ensembles (NODE), DNF-Net, and 1D-CNN deep learning models and compared their performance on eleven datasets with XGBoost. This is an important paper in the sense that it reiterates that deep learning may not be the silver bullet for solving all machine learning problems. On the other hand, tree-based algorithms have been shown to perform at par or even outperform neural networks for tabular data while being simple to use and comprehend. And there is good news for people who like to work with tree-based models.

In their paper, Tabular Data: Deep Learning is Not All You Need, the authors argue that while deep learning methods have shown tremendous success in the image and text domains, traditional tree-based methods like XGBoost still continue to shine when it comes to tabular data. The authors examined Tabnet, Neural Oblivious Decision Ensembles (NODE), DNF-Net, and 1D-CNN deep learning models and compared their performance on eleven datasets with XGBoost. This is an important paper in the sense that it reiterates that deep learning may not be the silver bullet for solving all machine learning problems. On the other hand, tree-based algorithms have been shown to perform at par or even outperform neural networks for tabular data while being simple to use and comprehend. And there is good news for people who like to work with tree-based models.