To tune or not to tune? An Approach for Recommending Important Hyperparameters

TL;DR

This paper investigates the importance of hyperparameters in machine learning models, providing insights and empirical results to guide when and how to tune hyperparameters effectively.

Contribution

It introduces a method to analyze the relationship between model performance and hyperparameters, aiding decisions on tuning efforts and hyperparameter space selection.

Findings

Gradient boosting and AdaBoost outperform other classifiers across datasets.

Tuning significantly improves the performance of certain classifiers.

Insights help decide whether to tune hyperparameters or focus on important ones.

Abstract

Novel technologies in automated machine learning ease the complexity of algorithm selection and hyperparameter optimization. Hyperparameters are important for machine learning models as they significantly influence the performance of machine learning models. Many optimization techniques have achieved notable success in hyperparameter tuning and surpassed the performance of human experts. However, depending on such techniques as blackbox algorithms can leave machine learning practitioners without insight into the relative importance of different hyperparameters. In this paper, we consider building the relationship between the performance of the machine learning models and their hyperparameters to discover the trend and gain insights, with empirical results based on six classifiers and 200 datasets. Our results enable users to decide whether it is worth conducting a possibly time-consuming tuning strategy, to focus on the most important hyperparameters, and to choose adequate hyperparameter spaces for tuning. The results of our experiments show that gradient boosting and Adaboost outperform other classifiers across 200 problems. However, they need tuning to boost their performance. Overall, the results obtained from this study provide a quantitative basis to focus efforts toward guided automated hyperparameter optimization and contribute toward the development of better-automated machine learning frameworks.