How does hyperparameter tuning affect model performance?

What is hyperparameter tuning and why is it important for machine learning models?

Hyperparameter tuning involves adjusting the parameters that govern the training process of a model. It's crucial because it can significantly enhance model performance by finding the optimal settings for learning.

How does it actually affect the performance of a model?

It affects performance by optimizing the model's ability to learn from data. Proper tuning can lead to better accuracy, reduced overfitting, and improved generalization to new data.

Can you give me an example of hyperparameters that I might tune?

Sure! Common hyperparameters include learning rate, batch size, number of epochs, and the architecture of neural networks, like the number of layers and units.

How do I know if my tuning is successful?

You can evaluate success by monitoring metrics like accuracy, precision, recall, or F1 score on a validation set. If these metrics improve, your tuning is likely successful.