Understand the implications of an AUC score of 0.3 in Azure ML Designer’s binary classification models. Learn about model performance and predictive accuracy for improved assessments!
Table of Contents
Question
You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model’s performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.3. What can you conclude about the model?
Answer
The model performs worse than random guessing.
Explanation
An AUC score of 0.3 for a binary classification model in Azure ML Designer indicates poor performance. An AUC (Area Under the Curve) score closer to 1 signifies better discrimination between classes, while a score around 0.5 suggests random chance. An AUC of 0.3 indicates the model’s predictions are largely incorrect or inversely related to the true labels.
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