Random forest for the real forests

Abstract

A forest is a vast area of land covered predominantly with trees and undergrowth. In this paper, adhering to cartographic variables, we try to predict the predominant kind of tree cover of a forest using the Random Forests (RF) classification method. The study classifies the data into seven classes of forests found in the Roosevelt National Forest of Northern Colorado. With sufficient data to create a classification model, the RF classifier gives reasonably accurate results. Fine-tuning of the algorithm parameters was done to get promising results. Besides that a dimensionality check on the dataset was conducted to observe the possibilities of dimensionality reduction.