Mining data with random forests: A survey and results of new tests
Abstract
Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration...
Paper Details
Title
Mining data with random forests: A survey and results of new tests
Published Date
Feb 1, 2011
Journal
Volume
44
Issue
2
Pages
330 - 349
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