# An Experimental Comparison of Old and New Decision Tree Algorithms

arXiv: https://arxiv.org/abs/1911.03054 · HTML: https://mlsanity.com/paper/1911.03054/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms](https://mlsanity.com/paper/2103.01096/) (2021)
- [Representation of binary classification trees with binary features by quantum circuits](https://mlsanity.com/paper/2108.13207/) (2021)
- [Backpropagation Neural Tree](https://mlsanity.com/paper/2202.02248/) (2022)
- [Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features](https://mlsanity.com/paper/2104.02922/) (2021)
- [Tree in Tree: from Decision Trees to Decision Graphs](https://mlsanity.com/paper/2110.00392/) (2021)
- [Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization](https://mlsanity.com/paper/2107.08761/) (2021)
- [End-to-end Feature Selection Approach for Learning Skinny Trees](https://mlsanity.com/paper/2310.18542/) (2023)

## Abstract (arXiv)

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.