Hyperparameter Importance Across Datasets (1710.04725v2)

Abstract: With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.

• The paper introduces a meta-learning framework using functional ANOVA to measure hyperparameter influence across diverse datasets.
• Key findings identify gamma and complexity for SVMs, minimum samples per leaf for Random Forests, and decision tree depth for AdaBoost as critical hyperparameters.
• Robust hyperparameter priors derived from data-driven techniques significantly improve tuning methods like Hyperband, streamlining AutoML processes.

Analyzing Hyperparameter Importance Across Datasets

The paper "Hyperparameter Importance Across Datasets" by Jan N. van Rijn and Frank Hutter addresses a fundamental aspect of automated machine learning: understanding which hyperparameters have the most influence on model performance, and identifying effective values for these hyperparameters. Given the growth of AutoML systems and automated hyperparameter optimization methods, this research aims to deliver insights that extend beyond simply finding performance-optimizing configurations. The authors propose a methodology based on meta-learning which is applied across a diverse set of datasets to quantify hyperparameter importance and recommend robust defaults or priors for hyperparameter values.

The paper employs functional ANOVA, a well-established tool for understanding variance contributions in a hyperparameter space, to discern which hyperparameters are consistently important across datasets. Through this approach, they examine common machine learning algorithms: Support Vector Machines (SVMs) with different kernels, Random Forests, and Adaboost. The method involves analyzing data from OpenML, leveraging a wide array of pre-existing experimental results to perform a robust analysis across multiple datasets.

Key Findings

1. Hyperparameter Importance:
   • For SVMs, the most critical hyperparameters identified are gamma and the complexity parameter. This affirms prevailing notions in machine learning literature about their significance in influencing model performance, although prior evidence was largely anecdotal.
   • In Random Forests, the minimum number of samples per leaf and the maximum number of features considered for a split were found to be the most impactful.
   • For Adaboost, the depth of the decision trees and the learning rate emerged as the most crucial settings.

The investigation into these hyperparameters showed a pattern that reaffirms folk knowledge but with empirical support across numerous datasets. Interestingly, this paper quantitatively supports these claims, providing a scientific basis for tuning these algorithms.

1. Setting Good Hyperparameter Values:
   • The authors developed kernel density estimators to infer priors over beneficial hyperparameter values from successful configurations across many datasets. Remarkably, results showed that these data-driven priors contribute to statistically significant improvements in hyperparameter tuning compared to uniform sampling.
   • Hyperband, a state-of-the-art optimization method, was used to evaluate the impact of these priors. The integration of data-driven priors into Hyperband consistently outperformed the baseline sampling on several datasets, validating the usefulness of these priors.

Implications

The outcomes of this research have significant implications. Firstly, they assist in streamlining the task of manual algorithm tuning, enabling practitioners to focus their efforts effectively by concentrating on the most pivotal hyperparameters identified for a given algorithm. Additionally, these insights could enhance automated hyperparameter optimization pipelines by incorporating learned priors, thus accelerating convergence to high-performance models and potentially saving computational resources.

Moreover, this methodology sets a precedent for future studies to continuously refine hyperparameter insights as more empirical data becomes available. Given its applicability to other machine learning paradigms, extending this approach to encompass models like neural networks could provide valuable insights, particularly for their notoriously large hyperparameter spaces.

In conclusion, van Rijn and Hutter's work on hyperparameter importance provides a significant empirical foundation for the field of meta-learning and AutoML. By quantitatively analyzing hyperparameter influences across multiple datasets, this research enhances our understanding and ability to optimize machine learning algorithms efficiently. The paper lays the groundwork for future developments in AI, particularly in creating more intelligent, self-tuning systems adaptable to various datasets and tasks.