What best describes the function of dropout in neural networks?

Dropout is a regularization technique used in neural networks to prevent overfitting, which occurs when a model learns the noise in the training data rather than the actual underlying patterns. During the training process, dropout works by randomly selecting a subset of neurons to temporarily deactivate or "drop out" at each iteration. This means that a different set of neurons is used for each training pass, introducing noise and encouraging the network to learn more robust features that are not reliant on any specific subset of neurons.

By ensuring that the model does not depend too heavily on any individual neuron, dropout helps to create a more generalized model that performs better on unseen data. This probabilistic deactivation forces the model to learn a broader range of features, which contributes to its ability to generalize, ultimately improving performance on the validation set.

The other options do not accurately capture the role of dropout in neural networks. While dropout does introduce some computational overhead due to the need to manage which neurons are active, its primary purpose is to enhance model generalization rather than speed. It does not reduce the dataset size, as it operates at the level of individual neurons in the network rather than on the data itself. Lastly, dropout does not involve dynamically adjusting weights; instead, it focuses on