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    • 1. 发明授权
    • Cascade boosting of predictive models
    • 级联提升预测模型
    • US06546379B1
    • 2003-04-08
    • US09427064
    • 1999-10-26
    • Se June HongBarry K. Rosen
    • Se June HongBarry K. Rosen
    • G06F1518
    • G06K9/6256G06N5/025G06N99/005
    • A method of boosting of predictive models, called cascade boosting, for resolving the interpretability problem of previous boosting methods while mitigating the fragmentation problem when applied to decision trees. This method of cascade boosting always applies a single weak model to any given data point. An improvement to the common method of boosting lies in how weak models are organized in a decision list rather than a weighted average. Cascade boosting resolves the interpretability problem of previous boosting methods while mitigating the fragmentation problem when applied to decision trees. Cascade boosting is simplest when applied to segmented predictive models but may also be applied to predictive models that do not explicitly segment the space of possible data points. The predictive model resulting from cascade boosting has fewer rules, or tree leaves, thereby enabling a modeler to better understand the correlations among the data.
    • 一种提高预测模型的方法,称为级联升压,用于解决先前的升压方法的可解释性问题,同时在应用于决策树时减轻分段问题。 这种级联升压的方法总是将单个弱模型应用于任何给定的数据点。 普遍提升方法的改进在于决策列表中组织的模型薄弱,而不是加权平均数。 级联提升解决了以前的提升方法的可解释性问题,同时在应用于决策树时减轻了碎片问题。 级联升压在应用于分段预测模型时最简单,但也可应用于未明确分割可能数据点空间的预测模型。 由级联提升产生的预测模型具有较少的规则或树叶,从而使建模者能够更好地了解数据之间的相关性。