期刊
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
卷 5, 期 6, 页码 853-862出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/nhess-5-853-2005
关键词
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The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting present and future landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data.
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