Hyperparameter Tuning

Hyperparameter tuning is the process of optimizing the configuration parameters of a machine learning model to improve its performance.

Hyperparameter tuning is commonly used in machine learning to find the best combination of hyperparameters that maximize the model’s performance on a given dataset. Hyperparameters describe the model’s characteristices. Hence, there are different from the parameters of a model, which are learned from the training data. Hyperparameter tuning is essential as they significantly influence the behavior and performance of the model and thus should be controlled with great care.

Hyperparameter tuning works by systematically searching through a predefined space of hyperparameters and evaluating the model’s performance using techniques like cross-validation. The goal is to identify the hyperparameter values that yield the best performance.

For example, consider a Random Forest model where hyperparameters include the number of trees and the maximum depth of each tree. Hyperparameter tuning can be used to find the optimal number of trees and tree depth that result in the highest accuracy on the validation set.

Common methods for hyperparameter tuning include exhaustive grid search, random search, and Bayesian optimization. Exhaustive grid search evaluates all possible combinations of hyperparameters, while random search samples a subset of hyperparameter combinations. Bayesian optimization uses probabilistic models to predict the performance of hyperparameter combinations and focuses on promising regions of the hyperparameter space. Some hyperparameters can also be deduced from the use case, such as the number of clusters for a clustering algorithm.

This chapter of the scikit-learn user guide provides an easy to follow introduction to tuning the hyper-parameters of an estimator.