Dataset Splitting

Dataset splitting is a critical step in the machine learning workflow where a dataset is divided into multiple subsets: typically training, validation, and test sets. The training set is used to train the model, the validation set helps in tuning hyperparameters, and the test set provides an unbiased evaluation of the model's performance. Proper splitting ensures that the model can generalize well to unseen data.