In Azure Machine Learning designer, the Split Data module is specifically designed to divide a dataset into training and validation (or testing) subsets. The AI-900 study guide and the Microsoft Learn module “Split data for training and evaluation” explain that this module allows users to control how data is partitioned, ensuring that models are trained on one portion of the data and tested on unseen data to assess performance.
By default, the Split Data module uses a 70/30 or 80/20 ratio, meaning 70–80% of the data is used for training and the remaining 20–30% for validation or testing. This ensures the model’s generalizability and prevents overfitting.
The other options serve different purposes:
A. Select Columns in Dataset: Used to choose specific columns or features from a dataset.
B. Add Rows: Combines multiple datasets vertically.
D. Join Data: Combines datasets horizontally based on a common key.
Only Split Data performs the function of dividing data into training and validation subsets.
[Reference:Microsoft Learn – Split data for training and evaluation in Azure Machine Learning designer, , , , , ]
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