Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Dropout is a regularization technique used in neural networks to prevent overfitting during the training phase. By randomly setting a fraction of the neurons to zero during training, dropout forces the model to learn more robust features that are less dependent on any single neuron. This process helps improve the generalization of the model, making it more reliable when applied to unseen data.

When dropout is utilized, it effectively alters the network architecture dynamically during training, which encourages different neurons to collaborate and share the learning burden, rather than relying on a specific set of neurons. This enhances the model's performance and reliability in various scenarios, as it does not become overly specialized on the training data.

In summary, dropout primarily affects the training phase of a neural network and enhances the model's reliability by fostering a more generalized understanding of the data, thereby reducing the risk of overfitting.