An AUC-based permutation variable importance measure for random forests
نویسندگان
چکیده
منابع مشابه
Permutation importance: a corrected feature importance measure
MOTIVATION In life sciences, interpretability of machine learning models is as important as their prediction accuracy. Linear models are probably the most frequently used methods for assessing feature relevance, despite their relative inflexibility. However, in the past years effective estimators of feature relevance have been derived for highly complex or non-parametric models such as support ...
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Random forests are a commonly used tool for classification with high-dimensional data as well as for ranking candidate predictors based on the so-called variable importance measures. There are different importance measures for ranking predictor variables, the two most common measures are the Gini importance and the permutation importance. The latter has been found to be more reliable than the G...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2013
ISSN: 1471-2105
DOI: 10.1186/1471-2105-14-119