  This paper presents a detailed comparison of a recently proposed algorithm
for optimizing decision trees, tree alternating optimization (TAO), with other
popular, established algorithms. We compare their performance on a number of
classification and regression datasets of various complexity, different size
and dimensionality, across different performance factors: accuracy and tree
size (in terms of the number of leaves or the depth of the tree). We find that
TAO achieves higher accuracy in nearly all datasets, often by a large margin.
