# Regularized impurity reduction: Accurate decision trees with complexity guarantees

arXiv: https://arxiv.org/abs/2208.10949 · HTML: https://mlsanity.com/paper/2208.10949/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Top-down induction of decision trees: rigorous guarantees and inherent limitations](https://mlsanity.com/paper/1911.07375/) (2019)
- [Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover](https://mlsanity.com/paper/1303.0726/) (2013)
- [On the Optimality of Trees Generated by ID3](https://mlsanity.com/paper/1907.05444/) (2019)
- [Scenario Submodular Cover](https://mlsanity.com/paper/1603.03158/) (2016)
- [On the boosting ability of top-down decision tree learning algorithm for multiclass classification](https://mlsanity.com/paper/1605.05223/) (2016)

## Built on by (most cited first, 3 of 3)

- [Efficient Exploration of the Rashomon Set of Rule Set Models](https://mlsanity.com/paper/2406.03059/) (2024)
- [Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection](https://mlsanity.com/paper/2409.03141/) (2024)
- [Learning Locally Interpretable Rule Ensemble](https://mlsanity.com/paper/2306.11481/) (2023)

## Abstract (arXiv)

Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model interpretability deteriorates. Traditional tree-induction algorithms, such as C4.5 and CART, rely on impurity-reduction functions that promote the discriminative power of each split. Thus, although these traditional methods are accurate in practice, there has been no theoretical guarantee that they will produce small trees. In this paper, we justify the use of a general family of impurity functions, including the popular functions of entropy and Gini-index, in scenarios where small trees are desirable, by showing that a simple enhancement can equip them with complexity guarantees. We consider a general setting, where objects to be classified are drawn from an arbitrary probability distribution, classification can be binary or multi-class, and splitting tests are associated with non-uniform costs. As a measure of tree complexity, we adopt the expected cost to classify an object drawn from the input distribution, which, in the uniform-cost case, is the expected number of tests. We propose a tree-induction algorithm that gives a logarithmic approximation guarantee on the tree complexity. This approximation factor is tight up to a constant factor under mild assumptions. The algorithm recursively selects a test that maximizes a greedy criterion defined as a weighted sum of three components. The first two components encourage the selection of tests that improve the balance and the cost-efficiency of the tree, respectively, while the third impurity-reduction component encourages the selection of more discriminative tests. As shown in our empirical evaluation, compared to the original heuristics, the enhanced algorithms strike an excellent balance between predictive accuracy and tree complexity.