What is hyperparameter optimization in machine learning?

What is hyperparameter optimization in machine learning?

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

Why are hyperparameters important?

Hyperparameters control the learning process and can significantly affect the model's accuracy and efficiency.

What are some common hyperparameters to optimize?

Common hyperparameters include learning rate, batch size, number of epochs, and the architecture of the model.

How do I perform hyperparameter optimization?

You can use techniques like grid search, random search, or more advanced methods like Bayesian optimization.