Performance comparison of random forests and extremely randomized trees
Date Issued
2016-04
Author(s)
Trajkovikj, Vladmir
Abstract
Random Forests (RF) recently have gained significant attention in the scientific community as simple, versatile
and efficient machine learning algorithm. It has been used for
variety of tasks due it its high predictive performance, ability to
perform feature ranking, its simple parallelization, and due to its
low sensitivity to parameter tuning. In recent years another treebased ensemble method has been proposed, namely the Extremely
Randomized Trees (ERT). These trees by definition have similar
properties. However, there is no extensive empirical evaluation
of both algorithms that would identify strengths and weaknesses
of each of them. In this paper we evaluate both algorithms of
several publicly available datasets. Our experiments show that
ERT are faster as the dataset size increases and can provide at
least the same level of predictive performance. As for feature
ranking capabilities, we have statistically confirmed that both
provide the same ranking, provided that the number of trees is
large enough.
and efficient machine learning algorithm. It has been used for
variety of tasks due it its high predictive performance, ability to
perform feature ranking, its simple parallelization, and due to its
low sensitivity to parameter tuning. In recent years another treebased ensemble method has been proposed, namely the Extremely
Randomized Trees (ERT). These trees by definition have similar
properties. However, there is no extensive empirical evaluation
of both algorithms that would identify strengths and weaknesses
of each of them. In this paper we evaluate both algorithms of
several publicly available datasets. Our experiments show that
ERT are faster as the dataset size increases and can provide at
least the same level of predictive performance. As for feature
ranking capabilities, we have statistically confirmed that both
provide the same ranking, provided that the number of trees is
large enough.
Subjects
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