Avoiding Overfitting of Decision Trees
作者： Max Bramer
DOI: 10.1007/978-1-4471-4884-5_9
关键词: Machine learning 、 Training set 、 Predictive power 、 Pruning (decision trees) 、 Computer science 、 Decision tree 、 Artificial intelligence 、 Overfitting
摘要: This chapter begins by examining techniques for dealing with clashes (i.e. inconsistent instances) in a training set. leads to discussion of methods avoiding or reducing overfitting decision tree data. Overfitting arises when is excessively dependent on irrelevant features the data result that its predictive power unseen instances reduced.
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