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2.3 Classification Models

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Microsoft Azure AI Fundamentals AI-900

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1 Introduction

About the Azure AI Fundamentals (AI-900) Exam Study Strategies and Exam Tips for the AI-900

2 Domain 1: Describe AI Workloads and Considerations (15-20%)

1.1 What Is Artificial Intelligence? 1.2 Responsible AI Principles 1.3 Identifying Common AI Workloads and Use Cases 1.4 Responsible AI Tools and Practices on Azure

3 Domain 2: Fundamental Principles of Machine Learning on Azure (20-25%)

2.1 Core Machine Learning Concepts 2.2 Regression Models 2.3 Classification Models 2.4 Clustering Models 2.5 Deep Learning and Neural Networks 2.6 Azure Machine Learning Service

4 Domain 3: Computer Vision Workloads on Azure (15-20%)

3.1 Computer Vision Concepts and Tasks 3.2 Azure AI Vision Service 3.3 Azure AI Custom Vision and Face Services 3.4 Azure AI Document Intelligence

5 Domain 4: Natural Language Processing Workloads on Azure (15-20%)

4.1 NLP Concepts and Common Tasks 4.2 Azure AI Language Service 4.3 Azure AI Speech Service 4.4 Azure AI Translator Service

6 Domain 5: Generative AI Workloads on Azure (15-20%)

5.1 Generative AI Fundamentals 5.2 Azure OpenAI Service 5.3 Prompt Engineering 5.4 Retrieval-Augmented Generation (RAG) 5.5 Responsible Generative AI 5.6 Microsoft Copilot and AI-Powered Applications

7 Exam Review and Full-Length Practice Questions

6.1 Domain Summary and Key Comparisons 6.2 Practice Questions: AI Workloads and Responsible AI 6.3 Practice Questions: Machine Learning 6.4 Practice Questions: Computer Vision, NLP, and Generative AI

2.3 Classification Models

Key Takeaways Explain This Topic AI

Classification models predict discrete categories (spam/not spam, disease/no disease) — the output is a class label, not a number.

Binary classification has two possible outcomes (yes/no); multi-class classification has three or more possible outcomes.

Key evaluation metrics: accuracy (overall correctness), precision (quality of positive predictions), recall (ability to find all positives), F1-score (balance of precision and recall).

A confusion matrix shows true positives, true negatives, false positives, and false negatives — it is the foundation for calculating all classification metrics.

The AI-900 tests conceptual understanding of classification — know what the metrics mean, not how to calculate them.

Last updated: June 2026

Quick Answer: Classification models predict discrete categories like spam/not spam or disease/no disease. Binary classification has two outcomes; multi-class has three or more. Evaluation metrics include accuracy, precision, recall, and F1-score. The confusion matrix shows true/false positives and negatives.

What Is Classification?

Classification is a supervised machine learning technique that predicts a discrete category (class) for each input. Unlike regression which outputs a number, classification outputs a label.

How to Identify a Classification Problem

Ask yourself: Is the predicted output a category or label?

  • Predicting if an email is spam or not → Classification (two categories)
  • Predicting a house price → Regression (continuous number)
  • Predicting the species of a flower (setosa, versicolor, virginica) → Classification (three categories)
  • Grouping customers by behavior → Clustering (unsupervised)
  • Binary vs. Multi-Class Classification
  • Binary Classification

Predicts one of two possible outcomes :

  • Use Case Class 0 (Negative) Class 1 (Positive)
  • Spam detection Not Spam Spam
  • Medical diagnosis No Disease Disease
  • Fraud detection Legitimate Fraudulent
  • Loan approval Deny Approve
  • Defect detection Good Defective
  • Multi-Class Classification

Predicts one of three or more possible outcomes :

  • Use Case Possible Classes
  • Image recognition Cat, Dog, Bird, Fish
  • Sentiment analysis Positive, Negative, Neutral
  • Customer segmentation Premium, Standard, Basic
  • Document classification Invoice, Receipt, Contract, Letter
  • Support ticket routing Billing, Technical, Account, General
  • The Confusion Matrix

The confusion matrix is a table that shows how well a classification model performs by comparing predictions to actual values. For binary classification:

  • Predicted: Positive Predicted: Negative
  • Actual: Positive True Positive (TP) False Negative (FN)
  • Actual: Negative False Positive (FP) True Negative (TN)
  • True Positive (TP): Model correctly predicts positive (detected spam that was actually spam)
  • True Negative (TN): Model correctly predicts negative (correctly identified a legitimate email)

False Positive (FP): Model incorrectly predicts positive (flagged a legitimate email as spam) — also called a "Type I error"

False Negative (FN): Model incorrectly predicts negative (missed actual spam) — also called a "Type II error"

On the Exam: Questions may present a scenario and ask which type of error is more concerning. For medical screening, false negatives (missing a disease) are typically worse than false positives (incorrectly flagging someone for further testing).

  • Classification Evaluation Metrics
  • Accuracy

The proportion of all predictions that are correct.

  • Formula: (TP + TN) / (TP + TN + FP + FN)
  • Interpretation: "What percentage of all predictions were correct?"

Limitation: Accuracy can be misleading with imbalanced datasets. If 95% of emails are not spam, a model that always predicts "not spam" has 95% accuracy but catches zero spam.

Precision

The proportion of positive predictions that are actually correct.

  • Formula: TP / (TP + FP)
  • Interpretation: "Of all the items I flagged as positive, how many actually were positive?"

When precision matters: When false positives are costly. Example: A spam filter with high precision rarely flags legitimate emails as spam.

Recall (Sensitivity)

The proportion of actual positives that the model correctly identifies.

  • Formula: TP / (TP + FN)
  • Interpretation: "Of all the actual positive items, how many did I find?"

When recall matters: When false negatives are costly. Example: A medical screening with high recall catches most actual disease cases.

F1-Score

The harmonic mean of precision and recall — a balanced measure.

Interpretation: A single metric that balances precision and recall. Useful when you need both to be high.

  • Metric Question It Answers When It Matters
  • Accuracy "How often is the model correct overall?" Balanced datasets
  • Precision "When the model says positive, is it right?" False positives are costly
  • Recall "Does the model find all positives?" False negatives are costly
  • F1-Score "Is there a good balance of precision and recall?" Both errors are costly
  • Precision vs. Recall Trade-off

There is often a trade-off between precision and recall:

  • Increasing precision (fewer false positives) often decreases recall (more false negatives)
  • Increasing recall (fewer false negatives) often decreases precision (more false positives)
  • Example: Medical screening

High recall priority: You want to catch EVERY case of a disease, even if some healthy people are flagged for follow-up tests (accept more false positives to minimize false negatives)

High precision priority: You only want to diagnose someone as sick when you are very confident (accept missing some cases to minimize false alarms)

On the Exam: Questions may describe a scenario and ask which metric is most important. Medical screening = recall (catch every case). Spam filter = precision (don't block legitimate emails). When both matter equally = F1-score.