  Classification is an important supervised machine learning method, which is
necessary and challenging issue for ecological research. It offers a way to
classify a dataset into subsets that share common patterns. Notably, there are
many classification algorithms to choose from, each making certain assumptions
about the data and about how classification should be formed. In this paper, we
applied eight machine learning classification algorithms such as Decision
Trees, Random Forest, Artificial Neural Network, Support Vector Machine, Linear
Discriminant Analysis, k-nearest neighbors, Logistic Regression and Naive Bayes
on ecological data. The goal of this study is to compare different machine
learning classification algorithms in ecological dataset. In this analysis we
have checked the accuracy test among the algorithms. In our study we conclude
that Linear Discriminant Analysis and k-nearest neighbors are the best methods
among all other methods
