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Thesis

Fast growing and interpretable oblique trees via logistic regression models

Abstract:

The classification tree is an attractive method for classification as the predictions it makes are more transparent than most other classifiers. The most widely accepted approaches to tree-growth use axis-parallel splits to partition continuous attributes. Since the interpretability of a tree diminishes as it grows larger, researchers have sought ways of growing trees with oblique splits as they are better able to partition observations. The focus of this thesis is to grow oblique trees in a ...

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Institution:
University of Oxford
Oxford college:
Christ Church
Department:
Mathematical,Physical & Life Sciences Division - Statistics
Role:
Author

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Role:
Supervisor
Publication date:
2009
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK
URN:
uuid:e0de0156-da01-4781-85c5-8213f5004f10
Local pid:
ora:3055

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