Dropout is a term used in education to describe a student who leaves school before completing their degree or diploma. This can happen for various reasons, including personal challenges, financial difficulties, or lack of interest in studies. Dropouts often face challenges in finding stable employment and may have lower earning potential compared to those who complete their education. In the context of machine learning, dropout is a regularization technique used to prevent overfitting in neural networks. During training, a certain percentage of neurons are randomly "dropped out" or ignored, which helps the model generalize better to new data. This technique improves the model's performance by making it more robust and less reliant on specific features.