
Contents
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Introduction Introduction
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Classical Statistics: Parametric Models Classical Statistics: Parametric Models
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Non-Classical Statistics: Data Mining Techniques Non-Classical Statistics: Data Mining Techniques
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Exemplary Techniques Exemplary Techniques
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Classification and Regression Trees Classification and Regression Trees
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Recursive Partitioning Recursive Partitioning
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Split Selection Criteria Split Selection Criteria
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Cut-Point Selection and Variable Selection Bias Cut-Point Selection and Variable Selection Bias
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Stopping and Pruning Stopping and Pruning
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Prediction and Interpretation Prediction and Interpretation
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Interpretation Interpretation
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Instability of Trees Instability of Trees
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Ensemble Methods Ensemble Methods
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Bagging Bagging
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Random Forests Random Forests
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Predictions From Ensembles Predictions From Ensembles
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Variable Importance Variable Importance
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Randomness Randomness
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Other Data Mining Techniques, Literature, and Software Other Data Mining Techniques, Literature, and Software
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Conclusion Conclusion
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Acknowledgment Acknowledgment
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Author Note Author Note
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References References
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29 Data Mining
Get accessCarolin Strobl, Ludwig-Maximilians-Universität München Faculty of Mathematics, Informatics and Statistics Institute of Statistics, Munich, Germany
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Published:01 October 2013
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Abstract
The term data mining refers to a variety of exploratory data analysis techniques developed in computer sciences and computational statistics. This chapter points out the commonalities and differences between data mining and classical statistical modeling. Common features of data mining techniques are then illustrated by means of one particular class of data mining techniques: the recursive partitioning methods classification and regression trees, bagging and random forests. In the end of the chapter an outlook on other popular data mining techniques as well as a short literature and software guide are given.
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