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A Study on Exploration of the Recommended Model of Decision Tree to Predict a Hard-to-Measure Mesurement in Anthropometric Survey
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 Title & Authors
A Study on Exploration of the Recommended Model of Decision Tree to Predict a Hard-to-Measure Mesurement in Anthropometric Survey
Choi, J.H.; Kim, S.K.;
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 Abstract
This study aims to explore a recommended model of decision tree to predict a hard-to-measure measurement in anthropometric survey. We carry out an experiment on cross validation study to obtain a recommened model of decision tree. We use three split rules of decision tree, those are CHAID, Exhaustive CHAID, and CART. CART result is the best one in real world data.
 Keywords
Decision tree;k-fold cross validation;CHAID;exhaustive CHAID;CART;
 Language
Korean
 Cited by
1.
이분형 목표변수의 분류 및 예측을 위한 최적모형의 탐지,김선경;최종후;

Journal of the Korean Data Analysis Society, 2014. vol.16. 1B, pp.115-124
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