id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_m56ahqpewbavvkcyoocqiqs75m	S PIRAMUTHU	Input data for decision trees	2008	7	.pdf	application/pdf	4537	610	73	Decision Trees are one of the popular methods that have been used for Data Mining purposes. constructing these decision trees assume no distributional patterns in the data (non-parametric), characteristics of the input data are usually not given much attention. We consider some characteristics of input data and their effect on the learning performance of decision Preliminary results indicate that the performance of decision trees can be improved with minor modifications of input data. and computational performance of Data Mining frameworks incorporating decision trees can be improved by Specifically, we consider the effects of non-linearity, outliers, heteroschedasticity, and multicollinearity in data. We consider the effects of non-linear data on decision trees both Input variables Decision tree size Prediction error (%) Input variables Decision tree size Prediction error (%) Input variables Decision tree size Prediction error (%) Input variables Decision tree size Prediction error (%) Evaluation of input data characteristics for decision trees	./cache/work_m56ahqpewbavvkcyoocqiqs75m.pdf	./txt/work_m56ahqpewbavvkcyoocqiqs75m.txt