What is hyperparameter tuning in machine learning?

What is hyperparameter tuning in machine learning?

Hyperparameter tuning is the process of optimizing the parameters that govern the training process of a machine learning model.

Why is it important to tune hyperparameters?

Tuning hyperparameters is crucial because it can significantly affect the model's performance and its ability to generalize to new data.

What are some common hyperparameters to tune?

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

How do I perform hyperparameter tuning?

You can perform hyperparameter tuning using techniques like grid search, random search, or more advanced methods like Bayesian optimization.

Can you give an example of hyperparameter tuning in practice?

Sure! For instance, when training a neural network, you might adjust the learning rate and batch size to find the best combination for your dataset.