A training set is a collection of data used to teach a machine learning model how to make predictions or decisions. This data typically includes input features and corresponding output labels, allowing the model to learn patterns and relationships within the data. The quality and size of the training set can significantly impact the model's performance. During the training process, the model analyzes the training set to adjust its parameters and improve accuracy. Once trained, the model can be evaluated using a separate test set to measure its effectiveness in making predictions on new, unseen data.