Machine learning hyperparameter tuning: eight commonly used methods

Machine learning algorithms require user-defined input to achieve a balance between accuracy and generality. This process is called hyperparameter tuning. There are various tools and methods for tuning hyperparameters.

We’ve compiled a list of the top eight methods for tuning machine learning model hyperparameters.

1. Bayesian Optimization

Bayesian optimization has become an effective tool for hyperparameter adjustment of machine learning algorithms, more specifically, for complex models such as deep neural networks. It provides an efficient framework to optimize expensive black-box functionality without knowing its form. It has been applied in many fields, including learning optimal robot mechanics, sequence experiment design, and synthetic gene design.

2. Genetic Algorithm

A genetic algorithm (EA) is an optimization algorithm that works by modifying a set of candidate solutions (population) according to certain rules called operators. One of the main advantages of EAs is their generality: this means that EAs can be used in a wide range of conditions because they are simple and independent of underlying problems. Genetic algorithms have been shown to perform better than accuracy/speed based grid search techniques in hyperparameter tuning problems.

3. Gradient-based optimization

Gradient-based optimization is a method of optimizing multiple hyperparameters based on machine learning model selection criteria relative to the gradient calculation of the hyperparameters. This hyperparameter tuning method can be applied when some differentiability and continuity conditions of the training criteria are met.

4. Grid search

Grid search is the basic method for hyperparameter tuning. It performs an exhaustive search over a user-specified set of hyperparameters. This method is the most direct and leads to the most accurate predictions. Using this tuning method, users can find the best combination. Grid search works for several hyperparameters, but the search space is limited.

5.Keras Tuner

Keras Tuner is a library that allows users to find optimal hyperparameters for machine learning or deep learning models. This library helps in finding kernel sizes, optimized learning rates, and different hyperparameters. Keras Tuner can be used to obtain optimal parameters for various deep learning models to achieve the highest accuracy.

6. Population-based optimization

Population-based methods are essentially a series of methods based on random search (such as genetic algorithms). One of the most widely used population-based methods is Population-Based Training (PBT) proposed by DeepMind. PBT is a unique approach in two aspects:

• It allows the use of adaptive hyperparameters during training
• It combines parallel search and sequential optimization

7.ParamILS

ParamILS (Iterative Local Search in Parameter Configuration Space) is a general random local search method for automatic algorithm configuration. ParamILS is an automated algorithm configuration method that facilitates the development of high-performance algorithms and their applications.

ParamILS is initialized with default and random settings and uses iterative first improvement as an auxiliary local search process. It also uses a fixed number of random moves for perturbation and always accepts better or equally good parameter configurations, but randomly reinitializes the search.

8. Random search

Random search can be said to be a basic improvement on grid search. This method refers to a random search for hyperparameters over some distribution of possible parameter values. The search process continues until the desired accuracy is achieved. Random search is similar to grid search but has been shown to create better results than the latter. This method is often used as a baseline for HPO to measure the efficiency of newly designed algorithms. Although random search is more efficient than grid search, it is still a computationally intensive method.

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