%0 Journal Article
	%A Ewa. M. Sztendur and  Neil T. Diamond
	%D 2012
	%J International Journal of Physical and Mathematical Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 71, 2012
	%T Using Fractional Factorial Designs for Variable Importance in Random Forest Models
	%U https://publications.waset.org/pdf/3038
	%V 71
	%X Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.

	%P 1612 - 1616