Discover the importance of the Split Data module in Azure ML Designer for unbiased validation of binary classification models. Explore effective model evaluation techniques!
Table of Contents
Question
You’re using Azure Machine Learning designer to create a training pipeline for a binary classification model. You’ve added a dataset containing features and labels, a Two-Class Decision Forest module, and a Train Model module. You plan to use Score Model and Evaluate Model modules to test the trained model with a subset of the dataset that wasn’t used for training. What’s another module should you add?
Answer
Split Data
Explanation
To validate the binary classification model effectively in Azure ML Designer, consider adding the Split Data module. This module divides the dataset into training and testing subsets, ensuring an untouched portion for model validation via the Score Model and Evaluate Model modules. By incorporating the Split Data module, you maintain a separate dataset subset solely for unbiased model evaluation.
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