what is dropout in machine learning?

Dropout is a regularization method that works well and is vital for reducing overfitting.

Sometimes, the nodes in a neural network create strong dependencies on other nodes, which may lead to overfitting. An example is when a few nodes on a layer do most of the work, and the network ignores all the other nodes. Despite having many nodes on the layer, you only have a small percentage of those nodes contributing to predictions. We call this phenomenon "co-adaptation," and we can tackle it using Dropout.

During training, Dropout randomly removes a percentage of the nodes, forcing the network to learn in a balanced way. Now every node is on its own and can't rely on other nodes to do their work. They have to work harder by themselves.

As you increase the Dropout rate, you will reduce overfitting and see the model's accuracy improve. At some point, however, the rate will be so high that the model will start underfitting, and the accuracy will come down. This drop happens because removing too many of the neurons during training will effectively reduce the model's capacity too much. In other words, while some regularization is good, too much of it will prevent the model from learning.