The train (or training) set is the portion of the training data that is used to train a machine learning model. It typically consists of 70-85% of the total available training data. It is essential to ensure that the training set is representative of the problem domain and includes a diverse set of examples. To maintain the integrity of the training process and avoid overfitting, it is essential to keep the training set separate from the validation and test sets. These other sets are used to fine-tune the model's hyperparameters and evaluate its performance on unseen data, respectively.