# Classic algorithms are fair learners: Classification Analysis of natural weather and wildfire occurrences

arXiv: https://arxiv.org/abs/2309.01381 · HTML: https://mlsanity.com/paper/2309.01381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Comparison of 14 different families of classification algorithms on 115 binary datasets](https://mlsanity.com/paper/1606.00930/) (2016)
- [Comparative Performance of Machine Learning Algorithms in Cyberbullying Detection: Using Turkish Language Preprocessing Techniques](https://mlsanity.com/paper/2101.12718/) (2021)
- [A study of tree-based methods and their combination](https://mlsanity.com/paper/2204.13916/) (2022)

## Abstract (arXiv)

Classic machine learning algorithms have been reviewed and studied mathematically on its performance and properties in detail. This paper intends to review the empirical functioning of widely used classical supervised learning algorithms such as Decision Trees, Boosting, Support Vector Machines, k-nearest Neighbors and a shallow Artificial Neural Network. The paper evaluates these algorithms on a sparse tabular data for classification task and observes the effect on specific hyperparameters on these algorithms when the data is synthetically modified for higher noise. These perturbations were introduced to observe these algorithms on their efficiency in generalizing for sparse data and their utility of different parameters to improve classification accuracy. The paper intends to show that these classic algorithms are fair learners even for such limited data due to their inherent properties even for noisy and sparse datasets.