Random Splitting Can be Fatal for ML Models

Randomly splitting data into training and validation sets can lead to data leakage, resulting in overfitting. Using techniques like GroupShuffleSplit in sklearn helps prevent this by grouping all related data points together and ensuring they end up in either the training or validation set. The method is illustrated using datasets with image captions and medical imaging, where specific features or identifiers are used as grouping criteria.