What Type of Machine Learning Predicts Customer Churn?
A retail company has historical data about customers, including age, purchase history, and whether they have churned (yes/no). They want to train a model that predicts if a new customer will churn. Which type of machine learning should they use?
Quick Answer
The answer is supervised classification, specifically binary classification, because the goal is to predict a categorical outcome—whether a customer will churn (yes or no)—using historical labeled data where the target variable is already known. This type of machine learning learns patterns from input features like age and purchase history to assign new customers to one of two discrete classes, making it the correct approach for churn prediction. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your ability to distinguish between supervised and unsupervised learning, with a common trap being to confuse regression (which predicts continuous values) with classification. Remember the key clue: if the output is a yes/no or true/false label, it is always supervised classification. A helpful memory tip is to think of “binary” as having two sides—like a coin flip—so whenever you see a two-category outcome, binary classification is your go-to model.
⚠ Common exam trap
Test-takers frequently confuse regression with classification when the output is a binary yes/no, mistakenly thinking any numeric prediction task is regression, but classification is required for discrete categorical outcomes.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Supervised classification
The goal is to predict a categorical outcome (churn: yes/no) from historical labeled data. Supervised classification algorithms, such as logistic regression or decision trees, learn from input features (age, purchase history) and the target label (churn status) to assign new customers to one of the discrete classes. This directly matches the requirement for a binary classification model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Supervised regression
Why it's wrong here
Regression predicts a continuous numeric value, not a binary category.
- ✓
Supervised classification
Why this is correct
Classification predicts a discrete category. Churn prediction is a classic binary classification problem.
- ✗
Unsupervised clustering
Why it's wrong here
Clustering finds hidden patterns in unlabeled data; it does not use predefined labels.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning uses an agent that learns by interacting with an environment and receiving rewards; it is not suitable for labeled historical data.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Same concept, more angles
1 more way this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is the role of a label (also called target or ground truth) in supervised machine learning?
easy- A.A category of input features used by the model
- ✓ B.The correct output or answer associated with each training example that the model learns to predict
- C.A text description attached to a model explaining what it does
- D.A tag applied to Azure ML resources for organization
Why B: In supervised machine learning, the label (also called target or ground truth) is the known correct output for each training example. The model uses these labels during training to learn the mapping from input features to outputs, enabling it to make accurate predictions on new, unseen data. This is fundamental to supervised learning, where the algorithm minimizes the error between its predictions and the ground truth labels.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.