What is the relationship between learning rate and overfitting in machine learning?

What is the relationship between learning rate and overfitting in machine learning?

The learning rate controls how much to change the model in response to the estimated error each time the model weights are updated. A high learning rate can lead to overfitting, as the model may converge too quickly to a suboptimal solution.

How can I adjust the learning rate to prevent overfitting?

You can use techniques like learning rate schedules or adaptive learning rate methods, such as Adam or RMSprop, to adjust the learning rate dynamically during training.

Are there any other factors that contribute to overfitting?

Yes, factors like model complexity, the amount of training data, and regularization techniques also play significant roles in overfitting.

What are some regularization techniques I can use?

Common regularization techniques include L1 and L2 regularization, dropout, and early stopping, which help to prevent overfitting by adding constraints to the model.