What is the difference between hyperparameters and parameters in machine learning?

What is the difference between hyperparameters and parameters in machine learning?

In machine learning, parameters are the internal variables of the model that are learned from the training data, while hyperparameters are the settings that are configured before the training process begins.

Can you give me an example of a hyperparameter?

Sure! An example of a hyperparameter is the learning rate, which determines how much to change the model in response to the estimated error each time the model weights are updated.

How do I choose the right hyperparameters?

Choosing the right hyperparameters often involves techniques like grid search, random search, or using algorithms like Bayesian optimization to find the best combination.

What happens if I set hyperparameters incorrectly?

If hyperparameters are set incorrectly, it can lead to poor model performance, such as overfitting or underfitting, which means the model won't generalize well to new data.