What are hyperparameters in machine learning?

What are hyperparameters in machine learning?

Hyperparameters are settings that govern the training process of a machine learning model, influencing its performance.

Why are they important?

They are crucial because they can significantly affect the model's accuracy and efficiency, impacting the final results.

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.

Can you give examples of common hyperparameters?

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