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

TL;DR

This paper empirically evaluates classic supervised learning algorithms on noisy, sparse weather and wildfire data, demonstrating their inherent fairness and robustness in classification tasks.

Contribution

It provides a detailed empirical analysis of classic algorithms' performance and fairness on sparse, noisy datasets, highlighting their robustness and parameter utility.

Findings

Classic algorithms maintain fairness on sparse, noisy data.

Hyperparameters significantly influence classification accuracy.

Algorithms demonstrate robustness despite data perturbations.

Abstract

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.