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