Real Python: Split Your Dataset With scikit-learn's train_test_split()

Supervised machine learning requires splitting datasets for unbiased evaluation and validation. Splitting datasets into training, validation, and test sets ensures impartial model assessment. Train_test_split() from scikit-learn's model_selection package facilitates data splitting. Random splitting minimizes bias and provides fresh data for evaluation. Validation sets assist in hyperparameter tuning, while test sets assess the final model. Training and test sets suffice when hyperparameter tuning is not necessary. Data splitting helps detect underfitting (poor performance on both training and test sets) and overfitting (good performance on training data but poor on unseen data). Scikit-learn version 1.5.0's model_selection package includes train_test_split(). Sklearn can be installed using pip. For more information and practical examples, refer to the full article.