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Chunk #136 — Features and Pitfalls — Tests for Variable Importance and Variable Selection

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An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.
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For selecting variables for further investigation in an exploratory study, we suggest a conservative decision aid for variable selection, already mentioned in the application example: All variables whose importance is negative, zero or has a small positive value that lies in the same range as the negative values, can be excluded from further exploration. The rationale for this rule of thumb is that the importance of irrelevant variables varies randomly around zero. Therefore positive variation of an amplitude comparable to that of negative variation does not indicate an informative predictor variable, while positive values that exceed this range may indicate that a predictor variable is informative.