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.
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Acknowledgments
The authors would like to thank KaggleFootnote 2 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 hostingFootnote 3 the dataset [16].
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Agrawal, S., Rana, S., Ahmad, T. (2016). Random Forest for the Real Forests. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 381. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2526-3_32
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DOI: https://doi.org/10.1007/978-81-322-2526-3_32
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