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Building a Classification Model on Azure ML

Master the fundamentals of supervised learning and deploy your first classification model using Azure Machine Learning.

February 5, 2026•10 min read•AI Instructor · Cloud Architect
AzureMachine LearningTutorial
Tutorial 10 min read

Building a Classification Model on Azure ML

Learn how to create a supervised classification model to categorize data into discrete labels using Azure Machine Learning.

🛠️ Prerequisites

  • • Azure Subscription
  • • Azure Machine Learning Workspace
  • • Basic understanding of ML concepts

Step 1: Understand Classification

Classification is a supervised learning task where models predict discrete labels. For example, predicting if an email is 'Spam' or 'Not Spam'.

Key metrics for evaluation include:

  • Precision: High precision means fewer false positives.
  • Recall: High recall means fewer false negatives.
  • F1-Score: Harmonic mean of precision and recall.

Step 2: Prepare Your Data

Ensure your data is split properly to avoid leakage. A standard split is:

  • Training Set: Used to fit the model.
  • Validation Set: Used to tune hyperparameters.
  • Test Set: Used for final evaluation.

Step 3: Training in Azure ML

Use Azure ML designer or SDK to select an algorithm (e.g., Logistic Regression, Decision Forest). Train the model on your compute instance and modify hyperparameters as needed.

Conclusion

By following these steps, you can deploy a robust classification model. Remember to always evaluate against a holdout test set to ensure your model generalizes well to new data.