Selecting Hyperparameters for Tree-Boosting

TL;DR

This paper empirically compares various hyperparameter optimization methods for tree-boosting, finding SMAC to be the most effective, and highlights the importance of extensive tuning and early stopping for accuracy.

Contribution

It provides a comprehensive empirical comparison of hyperparameter optimization methods for tree-boosting and offers practical insights on tuning strategies.

Findings

SMAC outperforms other hyperparameter optimization methods.

More than 100 trials are needed for accurate tuning.

Default hyperparameters lead to inaccurate models.

Abstract

Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters. In this article, we empirically compare several popular methods for hyperparameter optimization for tree-boosting including random grid search, the tree-structured Parzen estimator (TPE), Gaussian-process-based Bayesian optimization (GP-BO), Hyperband, the sequential model-based algorithm configuration (SMAC) method, and deterministic full grid search using $59$ regression and classification data sets. We find that the SMAC method clearly outperforms all the other considered methods. We further observe that (i) a relatively large number of trials larger than $100$ is required for accurate tuning, (ii) using default values for hyperparameters yields very inaccurate models, (iii) all considered hyperparameters can have a material effect on the accuracy of tree-boosting, i.e., there is no small set of hyperparameters that is more important than others, and (iv) choosing the number of boosting iterations using early stopping yields more accurate results compared to including it in the search space for regression tasks.