Why is hyperparameter tuning important in machine learning?

Why is hyperparameter tuning important in machine learning?

Hyperparameter tuning is crucial because it helps optimize the performance of machine learning models by finding the best parameters.

What are some common hyperparameters that need tuning?

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

How does hyperparameter tuning affect model accuracy?

Proper tuning can significantly improve model accuracy by ensuring the model learns effectively from the training data.

Can you explain the difference between hyperparameters and parameters?

Sure! Parameters are learned from the data during training, while hyperparameters are set before the training process begins.