Random Forest for the Real Forests

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)


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.


Random forests Dimensionality reduction Forests’ classification 



The authors would like to thank Kaggle2 for hosting the above problem. This dataset was provided by Jock A. Blackard and Colorado State University. We also thank the UCI machine learning repository for hosting3 the dataset [16].


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer EngineeringJamia Millia IslamiaNew DelhiIndia

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