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Tree Model Optimization Criterion without Using Prediction Error
National Agriculture and Food Research Organization, Agricultural Research Center Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan National Agriculture and Food Research Organization, Agricultural Research Center Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
- 1 National Agriculture and Food Research Organization, Agricultural Research Center Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan National Agriculture and Food Research Organization, Agricultural Research Center Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
Open Journal of Statistics·Volume 02 (2012)·Pages 478–483·Published 13 December 2012·DOI10.4236/ojs.2012.25061
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Abstract
The use of prediction error to optimize the number of splitting rules in a tree model does not control the probability of the emergence of splitting rules with a predictor that has no functional relationship with the target variable. To solve this problem, a new optimization method is proposed. Using this method, the probability that the predictors used in splitting rules in the optimized tree model have no functional relationships with the target variable is confined to less than 0.05. It is fairly convincing that the tree model given by the new method represents knowledge contained in the data.
KeywordsCross-ValidationModel Optimization CriterionOne-SE RuleSignificance LevelTree Model
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