TITLE:
Tree Model Optimization Criterion without Using Prediction Error
AUTHORS:
Kunio Takezawa
KEYWORDS:
Cross-Validation; Model Optimization Criterion; One-SE Rule; Significance Level; Tree Model
JOURNAL NAME:
Open Journal of Statistics,
Vol.2 No.5,
December
13,
2012
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.