Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Avoiding Overfitting of Decision Trees
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
This chapter begins by examining techniques for dealing with clashes (i.e. inconsistent instances) in a training set. This leads to a discussion of methods for avoiding or reducing
overfitting
of a decision tree to training data. Overfitting arises when a decision tree is excessively dependent on irrelevant features of the training data with the result that its predictive power for unseen instances is reduced.
Two approaches to avoiding overfitting are distinguished:
pre-pruning
(generating a tree with fewer branches than would otherwise be the case) and
post-pruning
(generating a tree in full and then removing parts of it). Results are given for pre-pruning using either a size or a maximum depth cutoff. A method of post-pruning a decision tree based on comparing the static and backed-up estimated error rates at each node is also described.