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作者：Frieman, Jerome H.; Popescu, Bogdan E.
作者单位：Stanford University
摘要：General regression and classification models are constructed as linear combinations of simple rules derived from the data. Each rule consists of a conjunction of a small number of simple statements concerning the values of individual input variables. These rule ensembles are shown to produce predictive accuracy comparable to the best methods. However, their principle advantage lies in interpretation. Because of its simple form, each rule is easy to understand, as it its influence on individual...
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作者：Diaconis, Persi; Goel, Sharad; Holmes, Susan
作者单位：Stanford University; Yahoo! Inc
摘要：Classical Multidimensional scaling (MDS) is a method for visualizing high-dimensional point clouds by mapping to low-dimensional Euclidean space. This mapping is defined in terms of eigenfunctions of a matrix of inter-point dissimilarities. In this paper we analyze in detail multidimensional scaling applied to a specific dataset: the 2005 United States House of Representatives roll call votes. Certain MDS and kernel projections output horseshoes that are characteristic of dimensionality reduct...
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作者：Ishwaran, Hemant; Kogalur, Udaya B.; Blackstone, Eugene H.; Lauer, Michael S.
作者单位：Cleveland Clinic Foundation; Columbia University; Cleveland Clinic Foundation; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
摘要：We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case ...
