Label distribution refers to the way different categories or labels are spread across a dataset. In machine learning, it helps to understand how many instances belong to each category, which can influence model training and performance. For example, if a dataset has a large number of instances labeled as cat compared to dog, the model may become biased towards predicting cat. Analyzing label distribution is crucial for tasks like classification and data balancing. If one label dominates, techniques such as oversampling or undersampling may be applied to ensure that the model learns effectively from all categories. This balance can lead to better predictions and a more robust model.