Dropout

Dropout is a technique used in machine learning, particularly in training neural networks. To improve the model's performance and prevent it from learning patterns too specific to the training data, dropout randomly "drops out," or ignores, a fraction of the neurons during each training step. This simulates different architectures and encourages the model to learn more robust features that are useful across various situations, rather than relying on specific neurons. Ultimately, dropout helps create models that generalize better to new, unseen data, improving their accuracy and effectiveness.