Investigation and Reduction of Discretization Variance in Decision Tree Induction
作者： Pierre Geurts , Louis Wehenkel
关键词: Reduction (complexity) 、 Interpretability 、 Discretization of continuous features 、 Stability (learning theory) 、 Decision tree 、 Mathematics 、 Statistics 、 Mathematical optimization 、 Discretization 、 Variance reduction 、 Variance (accounting)
摘要: This paper focuses on the variance introduced by discretization techniques used to handle continuous attributes in decision tree induction. Different procedures are first studied empirically, then means reduce proposed. The experiment shows that is large and it possible significantly without notable computational costs. resulting reduction mainly improves interpretability stability of trees, marginally their accuracy.
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