When splitting a dataset into training, validation, and test sets, prioritize clear separation of data roles and maintain representativeness. A common approach is to split the data into 60-20-20 or 80-10-10 ratios, depending on dataset size. For small datasets (e.g., 1,000 samples), a larger validation set (20%) helps ensure reliable evaluation during model tuning. The test set should remain untouched until final evaluation to prevent overfitting. Always shuffle data before splitting to avoid order-related biases—for example, if samples are sorted by class, a non-shuffled split might exclude entire categories from training.
Consider the data’s structure when choosing a splitting method. For imbalanced datasets (e.g., rare medical conditions), use stratified sampling to preserve class distributions across splits. Scikit-learn’s train_test_split has a stratify parameter for this purpose. For time-series data, avoid random splits; instead, use chronological ordering (e.g., first 70% of days for training, next 20% for validation, last 10% for testing). Grouped data (e.g., multiple samples from the same patient) requires splitting by group to prevent data leakage—use tools like GroupShuffleSplit to ensure all samples from a group stay in one set. If cross-validation is needed, apply it only to the training/validation portion, keeping the test set isolated.
Document and version your splits for reproducibility. Use fixed random seeds (e.g., random_state=42 in Python) to recreate identical splits during experiments. Save split indices or filenames to track which data points belong to each set. For large datasets, automate splitting with scripts that handle edge cases, such as missing values or duplicates. Tools like TensorFlow’s tf.data.Dataset or PyTorch’s SubsetRandomSampler can streamline this process. Always verify splits by checking summary statistics (e.g., class distributions, feature ranges) to confirm they’re representative of the full dataset.