Tree Model Optimization Criterion without Using Prediction Error

• 作者: Kunio Takezawa
• 来源期刊: 统计学期刊(英文)
• 学科: 数学
• 关键词: Cross-Validation MODEL Optimization CRITERION One-SE RULE SIGNIFICANCE Level TREE MODEL
• 年，卷（期）: 2012,（5）
• 页码范围: 478-483
• 页数: 6页
• 分类号: O1

摘要

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.