Random Forests as an extension of the classification trees with the R and Python programs
DOI:
https://doi.org/10.26439/interfases2017.n10.1775Keywords:
Random Forest, classification trees, non-parametric classification models, supervised learning, R language, Python languageAbstract
This article presents the application of the non-parametric Random Forest method through supervised learning, as an extension of classification trees. The Random Forest algorithm arises as the grouping of several classification trees. Basically it randomly selects a number of variables with which each individual tree is constructed and predictions are made with these variables that will later be weighted through the calculation of the most voted class of these trees that were generated, to finally do the prediction by Random Forest. For the application, we worked with 3168 recorded voices, for which the results of an acoustic analysis are presented, registering variables such as frequency, spectrum, modulation, among others, seeking to obtain a pattern of identification and classification according to gender through a voice identifier. The data record used is in open access and can be downloaded from the Kaggle web platform via <https://www.kaggle.com/primaryobjects/voicegende>r. For the development of the algorithm’s model, the statistical program R was used. Additionally, applications were made with Python by the development of classification algorithms.
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Last updated 03/05/21
