Hollow-tree Super：一种提升树模型中特征重要性的定向和可扩展方法
Hollow-tree Super: a directional and scalable approach for feature importance in boosted tree models
摘要：
目前在提升树建模的局限性，防止有效的缩放到数据集具有大量的功能，特别是当调查的大小和方向性的各种功能分类。我们提出了一种新的方法，空心树超级（HOTS），解决和可视化功能的重要性提升树模型涉及大量的功能。此外，HOTS允许调查各种特征对分类的方向性和幅度。使用Iris数据集，我们首先将HOTS与Gini Importance，Partial Dependence Plots和Permutation Importance进行比较，并演示HOTS如何解决这些方法中存在的弱点。然后，我们展示了如何HOTS可以利用在高维神经科学数据，采取60精神分裂症的主题和应用的方法，以确定哪些大脑区域是最重要的精神分裂症的分类所确定的PANSS。HOTS有效地复制和支持了Iris数据集中的基尼重要性、部分依赖图和排列重要性的发现。当应用于精神分裂症患者的大脑数据集时，HOTS能够解决前10个最重要的分类特征，以及与其他特征相比，它们的分类方向性和幅度。交叉验证支持这10个相同的特征在多棵树的决策过程中一直使用，这些特征主要局限于枕叶和顶叶皮质，这是精神分裂症患者常见的大脑区域。当务之急是开发一种方法，能够处理包含大量特征的大型数据集的工作需求。HOTS代表了一种独特的方式来调查的方向性和规模的功能的重要性时，在规模与提升树建模。
英文摘要：
Current limitations in boosted tree modelling prevent the effective scaling to datasets with a large feature number, particularly when investigating the magnitude and directionality of various features on classification. We present a novel methodology, Hollow-tree Super (HOTS), to resolve and visualize feature importance in boosted tree models involving a large number of features. Further, HOTS allows for investigation of the directionality and magnitude various features have on classification. Using the Iris dataset, we first compare HOTS to Gini Importance, Partial Dependence Plots, and Permutation Importance, and demonstrate how HOTS resolves the weaknesses present in these methods. We then show how HOTS can be utilized in high dimensional neuroscientific data, by taking 60 Schizophrenic subjects and applying the method to determine which brain regions were most important for classification of schizophrenia as determined by the PANSS. HOTS effectively replicated and supported the findings of Gini importance, Partial Dependence Plots and Permutation importance within the Iris dataset. When applied to the schizophrenic brain dataset, HOTS was able to resolve the top 10 most important features for classification, as well as their directionality for classification and magnitude compared to other features. Cross-validation supported that these same 10 features were consistently used in the decision-making process across multiple trees, and these features were localised primarily to the occipital and parietal cortices, commonly disturbed brain regions in those with Schizophrenia. It is imperative that a methodology is developed that is able to handle the demands of working with large datasets that contain a large number of features. HOTS represents a unique way to investigate both the directionality and magnitude of feature importance when working at scale with boosted-tree modelling.
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