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 fast and deterministic manner and to propose ways of making them more interpretable. Finding good oblique splits is a computationally difficult task. Various authors have proposed ways of doing this by either performing stochastic searches or by solving problems that effectively produce oblique splits at each stage of tree-growth. A new approach to finding such splits is proposed that restricts attention to a small but comprehensive set of splits. Empirical evidence shows that good oblique splits are found in most cases. When observations come from a small number of classes, empirical evidence shows that oblique trees can be grown in a matter of seconds. As interpretability is the main strength of classification trees, it is important for oblique trees that are grown to be interpretable. As the proposed approach to finding oblique splits makes use of logistic regression, well-founded variable selection techniques are introduced to classification trees. This allows concise oblique splits to be found at each stage of tree-growth so that oblique trees that are more interpretable can be directly grown. In addition to this, cost-complexity pruning ideas which were developed for axis-parallel trees have been adapted to make oblique trees more interpretable. A major and practical component of this thesis is in providing the oblique.tree package in R that allows casual users to experiment with oblique trees in a way that was not possible before.

No description available.