AutoML automates data splitting by handling the division of datasets into training, validation, and test sets without requiring manual configuration. This process ensures that machine learning models are trained on one subset of data, validated on another to tune hyperparameters, and tested on a final holdout set to evaluate performance. Most AutoML tools use predefined rules or adaptive strategies to split data effectively. For example, a common approach is to randomly allocate 70-80% of the data for training, 10-15% for validation, and 10-15% for testing. AutoML frameworks often include checks to ensure balanced class distributions in classification tasks, such as stratified sampling, which preserves the ratio of target classes across splits. This prevents scenarios where a rare class might be underrepresented in the training set, which could harm model accuracy.
The automation also accounts for dataset characteristics like size, temporal dependencies, or domain-specific requirements. For instance, if the data has a time-based component (e.g., sales records), AutoML might enforce chronological splitting to avoid training on future data during validation. Similarly, for imbalanced datasets, some tools automatically apply techniques like oversampling the minority class in the training set or adjusting split ratios to ensure all classes are sufficiently represented. AutoML platforms may also use cross-validation strategies, such as k-fold, where the data is partitioned into multiple subsets to iteratively train and validate models. For example, a 5-fold cross-validation splits data into five parts, using four for training and one for validation in each iteration, then aggregates results. This reduces variance in performance estimates, especially useful for smaller datasets.
Developers can often customize the splitting process through parameters, though AutoML provides sensible defaults. For example, tools like Google AutoML or H2O.ai allow users to specify the validation/test set size, random seed for reproducibility, or disable stratification if needed. AutoML also handles edge cases, such as detecting and removing duplicate entries that could leak information between splits. Some systems even analyze data dependencies—like patient records with multiple entries—to ensure all data from a single entity stays in one split. By automating these steps, AutoML reduces the risk of human error in data preparation while maintaining flexibility for developers to override defaults when domain knowledge dictates a specific approach. This balance between automation and configurability streamlines workflows without sacrificing control.