“BOF”树图作为一种可视化的方式来提高树组合的可解释性

V. Luzar-Stiffler, C. Stiffler
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引用次数: 0

摘要

本研究的动机源于创建视觉辅助工具的愿望,以帮助研究人员/管理人员解释由各种算法生成的决策树输出的集合。该方法采用模拟实验(仅使用套袋),然后将新的可视化工具应用于实际调查数据。具有预先指定结构的模拟数据被“打包”,结果使用五种图形工具呈现,这些工具重新创建(和/或描绘)由打包算法捕获的已知数据结构。然后将相同的方法推广到结构未知的原始(调查)数据集。研究的结果是检查了五种视觉辅助工具(其中两种是新方法),并发现它们有助于从例如网络调查数据中做出面向行动的解释
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"BOF" trees diagram as a visual way to improve interpretability of tree ensembles
The motivation for this research stemmed from a desire to create visual aids to help researchers/managers interpret ensembles of decision tree outputs generated by various algorithms. The method employed a simulation experiment (using only bagging) followed by application of the new visualization tools on actual survey data. Simulated data, with a prespecified structure, were "bagged" with the results presented using five graphical tools that recreated (and/or portrayed) the known data structures captured by the bagging algorithm. Then the same methodology was generalized to a structurally unknown, virgin (survey) data set. Results of the research are that five visual aids tools were examined (two of which are new approaches) and found to be useful for making action oriented interpretations from e.g., Web-survey data
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