Dive into our e-book, "Why do tree-based models still outperform deep learning on typical tabular data?" for a deep analysis. Explore why, despite advances in deep learning, tree-based models like XGBoost and Random Forest consistently outperform it on tabular data. Get insights on performance benchmarking, model comparisons, observed patterns, and lessons for deep learning. This study aims to guide future research in bridging this performance gap.
Performance Benchmarking of Tree-based Models vs Deep Learning
Explore the performance gap between tree-based models and deep learning techniques on tabular data, and understand why the former consistently outperforms the latter.
Detailed Comparison of Various Model Architectures
Explore why tree-based models consistently outperform deep learning on tabular data and how this impacts data analysis practices.
Identification of Tree-Based Models' Superior Performance
This e-book explores why tree-based models continue to outperform deep learning in handling typical tabular data despite technological advances.
Investigating Inductive Biases in Model Performance
Explore why tree-based models consistently outperform deep learning on tabular data, through performance benchmarks, detailed analyses and future directions.
Lessons and Recommendations for Deep Learning Models
Explore why tree-based models consistently outperform deep learning on tabular data, and the improvements needed for deep learning to excel in this domain.
Possible Future Directions and Contributions of Research
Explore why tree-based models outdo deep learning in tabular data handling, with insights into future research directions and potential improvements.
