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Random Selection of Variables for Aggregated Tree-Based Models

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Gatnar E., Rozmus D., The Karol Adamiecki University of Economics K.

Acta Universitatis Lodziensis. Folia Oeconomica

2006

vol. 196

Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 zostało dofinansowane ze środków MNiSW w ramach działalności upowszechniającej naukę.

Tree-based models are popular a widely used because they are simple, flexible and powerful tools for classification. Unfortunately they are not stable classifiers. Significant improvement of the model stability and prediction accuracy can be obtained by aggregation of multiple classification trees. Proposed methods, i.e. bagging, adaptive bagging, and arcing are based on sampling cases from the training set while boosting uses a system of weights for cases. The result is called committee of trees, an ensemble or a forest. Recent developments in this field showed that randomization (random selection of variables) in aggregated tree-based classifiers leads to consistent models while boosting can overfit. In this paper we discuss optimal parameter values for the method of random selection of variables (RandomForest) for an aggregated tree-based model (i.e. number of trees in the forest and number of variables selected for each split).

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1 Acta Universitatis Lodziensis. Folia Oeconomica

1 Gatnar E.

1 Rozmus D.

1 The Karol Adamiecki University of Economics K.

1 2006

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Random Selection of Variables for Aggregated Tree-Based Models