# Classic machine learning methods

arXiv: https://arxiv.org/abs/2310.11470 · HTML: https://mlsanity.com/paper/2310.11470/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 29 references that are in the map.
- First posted on arXiv 24 May 2023.

## Built on by (most cited first, 2 of 2)

- [Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey](https://mlsanity.com/paper/2509.21389/) (2025)
- [Assessing Classical Machine Learning and Transformer-based Approaches for Detecting AI-Generated Research Text](https://mlsanity.com/paper/2509.20375/) (2025)

## Abstract (arXiv)

In this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest-neighbor methods, linear and logistic regressions, support vector machines and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely for clustering and dimensionality reduction.