When splitting a dataset into training, validation, and test sets, the main goal is to ensure that each subset serves its specific purpose while maintaining the integrity of the data. The training set is used to train the model, the validation set helps in tuning model parameters, and the test set evaluates the final model's performance. A common practice is to use an 80-10-10 split, where 80% of the data is allocated to training, 10% to validation, and 10% to testing. This helps balance the data used for training the model while still leaving enough for evaluation purposes.
It’s important to ensure that each subset is representative of the overall dataset. One effective approach is to perform stratified sampling, especially when dealing with imbalanced classes. For instance, if you're working on a classification problem with three classes where one class is significantly larger than the others, make sure that each subset contains a proportionate number of examples from each class. This maintains the underlying distribution and helps the model perform better across all classes. Additionally, if you are using time series data, you should respect the temporal order of the data to avoid data leakage, which means the training set should contain earlier instances while the test set includes later instances.
Finally, always consider the size of your dataset when splitting. In small datasets, even a small fraction set aside for testing can have a major impact on evaluation. To mitigate this, techniques like k-fold cross-validation can be used, where the dataset is divided into k subsets. The model is trained on k-1 subsets and tested on the remaining subset, and this process is repeated k times. This gives a more robust estimate of the model performance and helps maximize the use of limited data. In conclusion, carefully planning your data splits while considering representation, balance, and validation methods is crucial in building effective machine learning models.