Data splitting is a crucial step in the machine learning process, where the available dataset is divided into different subsets to train and evaluate models effectively. Typically, the dataset is split into at least two parts: a training set and a testing set. The training set is used to train the model, while the testing set is reserved for evaluating its performance on unseen data. This helps ensure that the model generalizes well to new data rather than just memorizing the training data.

In more advanced practices, it is recommended to split the dataset into three parts: a training set, a cross-validation set (or development set), and a testing set. The training set is the largest, while the cross-validation set is used for hyperparameter tuning, and the testing set provides an unbiased estimate of the model’s performance in real-world scenarios. The typical ratio for splitting can vary, but a common approach is 60% for training, 20% for cross-validation, and 20% for testing, especially when the dataset size is moderate ⁴.

To avoid data leakage, it is essential to split the data before any preprocessing steps, ensuring that the model does not gain access to information from the testing set during training ³.