SigOpt Mulch: An Intelligent System for AutoML of Gradient Boosted Trees

TL;DR

SigOpt Mulch is an automated, model-aware hyperparameter tuning system for gradient boosted trees that leverages metalearning and multifidelity optimization to improve efficiency and reduce domain knowledge requirements.

Contribution

It introduces a model-aware hyperparameter tuning system for GBTs that automates search space learning and enhances optimization efficiency.

Findings

Outperforms existing black-box tuning systems in efficiency.

Reduces need for domain knowledge in hyperparameter tuning.

Automates hyperparameter search space learning.

Abstract

Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable behavior, and ease-of-use. One critical challenge in training GBTs is the tuning of their hyperparameters. In practice, selecting these hyperparameters is often done manually. Recently, the ML community has advocated for tuning hyperparameters through black-box optimization and developed state-of-the-art systems to do so. However, applying such systems to tune GBTs suffers from two drawbacks. First, these systems are not \textit{model-aware}, rather they are designed to apply to a \textit{generic} model; this leaves significant optimization performance on the table. Second, using these systems requires \textit{domain knowledge} such as the choice of hyperparameter search space, which is an antithesis to the automatic experimentation that black-box optimization aims to provide. In this paper, we present SigOpt Mulch, a model-aware hyperparameter tuning system specifically designed for automated tuning of GBTs that provides two improvements over existing systems. First, Mulch leverages powerful techniques in metalearning and multifidelity optimization to perform model-aware hyperparameter optimization. Second, it automates the process of learning performant hyperparameters by making intelligent decisions about the optimization search space, thus reducing the need for user domain knowledge. These innovations allow Mulch to identify good GBT hyperparameters far more efficiently -- and in a more seamless and user-friendly way -- than existing black-box hyperparameter tuning systems.