What is dropout in machine learning?

What is dropout in machine learning?

Dropout is a regularization technique used to prevent overfitting in neural networks by randomly dropping units during training.

How does dropout help in improving model performance?

By preventing the model from becoming too reliant on any specific neurons, dropout encourages a more robust feature representation.

Are there any downsides to using dropout?

Yes, if the dropout rate is too high, it can lead to underfitting, where the model fails to learn the underlying patterns.

What is a typical dropout rate to use?

Common dropout rates range from 20% to 50%, but it often depends on the specific architecture and dataset.