AutoML automates data splitting by using predefined strategies that enhance the machine learning workflow while minimizing manual efforts. In general, data splitting refers to dividing a dataset into different subsets—typically training, validation, and test sets. By doing this, models can be trained and evaluated effectively without overfitting. AutoML platforms often have built-in mechanisms to automatically select the most suitable splitting technique for the given dataset.
One common technique used in AutoML is stratified splitting. This method ensures that the distribution of class labels in the training and validation sets is representative of the overall dataset. For example, if you have a dataset containing 70% of instances belonging to class A and 30% to class B, stratified splitting will maintain this ratio in both the training and validation sets. AutoML tools apply this technique seamlessly, saving developers from having to write the code to perform this operation manually.
In addition to stratified splitting, AutoML also uses k-fold cross-validation as a form of data splitting. This technique divides the dataset into k equal subsets and trains the model k times, each time using a different subset for validation and the others for training. This method helps ensure more robust evaluation metrics by reducing variance associated with a single train-test split. Once again, developers can rely on AutoML to implement this without needing to handle the complexities involved, allowing them to focus on other aspects of model development.