Extracting optimal actionable plans from additive
tree models
Qiang LU;Zhicheng CUI;Yixin CHEN;Xiaoping CHEN
【期刊名称】《中国高等学校学术文摘·计算机科学》 【年(卷),期】2017(011)001
【摘要】Although amazing progress has been made in machine learning to achieve high generalization accuracy and efficiency,there is still very limited work on deriving meaningful decision-making actions from the resulting
models.However,in
many
applications
such
as
advertisement,recommendation systems,social networks,customer
relationship management,and clinical prediction,the users need not only accurate prediction,but also suggestions on actions to achieve a desirable goal (e.g.,high ads hit rates) or avert an undesirable predicted result (e.g.,clinical deterioration).Existing works for extracting such actionability are few and limited to simple models such as a decision tree.The dilemma is that those models with high accuracy are often more complex and harder to extract actionability from.In this paper,we propose an effective method to extract actionable knowledge from additive tree models (ATMs),one of the most widely used and best off-the-shelf classifiers.We rigorously formulate the optimal actionable planning (OAP) problem for a given ATM,which is to extract an actionable plan for a given input so that it can achieve a desirable
Extracting optimal actionable plans from additive tree models
