Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges

by   Bernd Bischl, et al.

Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance. To avoid a time consuming and unreproducible manual trial-and-error process to find well-performing hyperparameter configurations, various automatic hyperparameter optimization (HPO) methods, e.g., based on resampling error estimation for supervised machine learning, can be employed. After introducing HPO from a general perspective, this paper reviews important HPO methods such as grid or random search, evolutionary algorithms, Bayesian optimization, Hyperband and racing. It gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with ML pipelines, runtime improvements, and parallelization.


page 11

page 23

page 27

page 28


Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter

We propose an algorithm for a family of optimization problems where the ...

Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers

Automated hyperparameter optimization (HPO) has gained great popularity ...

Quantifying error contributions of computational steps, algorithms and hyperparameter choices in image classification pipelines

Data science relies on pipelines that are organized in the form of inter...

One-Shot Bayes Opt with Probabilistic Population Based Training

Selecting optimal hyperparameters is a key challenge in machine learning...

An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML

A common claim of evolutionary computation methods is that they can achi...

DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization

Modern machine learning algorithms crucially rely on several design deci...