Introduction to Hyperparameter Optimization

What is hyperparameter optimization?

Hyperparameter optimization, also known as hyperparameter tuning, is the process of finding the best combination of hyperparameters for a given model and dataset to achieve maximum performance in a reasonable amount of time. This can be done through a manual process of trial and error. However, it is often more efficient to use automated techniques such as grid search, random search, or Bayesian optimization.

Most of these ML algorithms come with the default hyperparameter values, and they often don’t produce ML models with the best performance. Therefore, optimizing their values can help to identify the best combination that can produce the best performance.

Optimization of hyperparameters is regarded as the most challenging aspect of developing ML models.

Note: A well-chosen set of hyperparameters can make an algorithm truly shine.

Hyperparameter optimization is considered the trickiest part of building ML models.

Why is hyperparameter optimization important?

It is important to learn and understand hyperparameter optimization techniques for the following reasons:

Improves the performance of the ML model