Splitting data into training, validation, and test sets is a fundamental step in developing reliable machine learning models. The purpose of this split is to ensure that the model learns effectively, is fine-tuned appropriately, and is evaluated fairly.
The training set is the core portion of the data used to teach the model how to make predictions. It contains examples with known outcomes, allowing the model to adjust its internal parameters based on the relationships it identifies in the data. Typically, the majority of the data—often around 60% to 70%—is allocated to this set, though the exact proportion can vary depending on the dataset size and the complexity of the model.
After training, the validation set serves as a checkpoint for tuning the model. It is not used during training but plays a key role in model selection, hyperparameter tuning, and early stopping. By evaluating model performance on this set, one can assess whether changes—like altering learning rates or regularization strength—are improving the model or leading to overfitting. The validation set provides insight into how the model is generalizing to data it hasn’t seen during training but is still accessible during the development phase.
Finally, the test set acts as a final exam for the model. Once all training and tuning are complete, the test set is used to measure the model’s performance on truly unseen data. This provides an unbiased estimate of how well the model is likely to perform in the real world. The test set must remain untouched until the very end to preserve its integrity as a benchmark.
In practice, the split can be done randomly, but it’s important to ensure that the data remains representative of the whole. For classification tasks, stratified sampling is often used to maintain consistent class distributions across each subset. In time series problems, the split should respect chronological order to prevent information leakage from future data.
For small datasets, a single validation set may not be sufficient, and techniques like k-fold cross-validation can be employed. In that approach, the data is divided into k parts, and the model is trained and validated k times, each time with a different partition as the validation set. This helps to ensure robustness in performance estimates.
The way data is split plays a crucial role in the success of a machine learning project. When done thoughtfully, it supports the development of models that are both accurate and generalizable, minimizing surprises when they are deployed in real-world applications.