# Machine Learning vs Deep Learning: The Generalization Problem

arXiv: https://arxiv.org/abs/2403.01621 · HTML: https://mlsanity.com/paper/2403.01621/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 1 of 1)

- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)

## Built on by (most cited first, 6 of 6)

- [When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling](https://mlsanity.com/paper/2606.28661/) (2026)
- [A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases](https://mlsanity.com/paper/2410.20293/) (2024)
- [Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression](https://mlsanity.com/paper/2606.23575/) (2026)
- [GRPO, Dr. GRPO, and DAPO Are Three Operations on One Number: The Group-Standard-Deviation Identity](https://mlsanity.com/paper/2607.00152/) (2026)
- [AdCare-VLM: Towards a Unified and Pre-aligned Latent Representation for Healthcare Video Understanding](https://mlsanity.com/paper/2505.00275/) (2025)
- [Secret Scanner Agent: Extracting Secrets and Access Context from Unstructured Documents](https://mlsanity.com/paper/2607.09011/) (2026)

## Abstract (arXiv)

The capacity to generalize beyond the range of training data is a pivotal challenge, often synonymous with a model's utility and robustness. This study investigates the comparative abilities of traditional machine learning (ML) models and deep learning (DL) algorithms in terms of extrapolation -- a more challenging aspect of generalization because it requires the model to make inferences about data points that lie outside the domain it has been trained on. We present an empirical analysis where both ML and DL models are trained on an exponentially growing function and then tested on values outside the training domain. The choice of this function allows us to distinctly showcase the divergence in performance when models are required to predict beyond the scope of their training data. Our findings suggest that deep learning models possess inherent capabilities to generalize beyond the training scope, an essential feature for real-world applications where data is often incomplete or extends beyond the observed range. This paper argues for a nuanced understanding of the structural differences between ML and DL models, with an emphasis on the implications for both theoretical research and practical deployment.