Thesis
Fast growing and interpretable oblique trees via logistic regression models
- Abstract:
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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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Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Truong, A
Bibliographic Details
- Publication date:
- 2009
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- Oxford University, UK
Item Description
- Language:
- English
- Keywords:
- Subjects:
- UUID:
-
uuid:e0de0156-da01-4781-85c5-8213f5004f10
- Local pid:
- ora:3055
- Deposit date:
- 2009-11-10
Terms of use
- Copyright holder:
- Truong, A
- Copyright date:
- 2009
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