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, whereas churn is a binary yes/no label. It would be correct for forecasting spend or lifetime value, but classification is required to assign customers to churned or retained categories.
- ✓
Supervised classification
Why this is correct
The churn label is binary (yes/no) and historical records already contain known outcomes, so the model learns a mapping from features such as age and purchase history to that label. This is supervised classification, not clustering or regression.
- ✗
Unsupervised clustering
Why it's wrong here
Clustering groups unlabelled data by similarity and discovers structure without predefined classes. The dataset already contains known churn labels, so a supervised classification algorithm is needed; clustering would suit segmenting customers when no outcome field exists.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning learns from reward signals through trial-and-error interaction, not from labelled historical churn records. It suits sequential decision problems such as robotics or game playing, where an agent optimises actions over time rather than classifying existing examples.
Go deeper
Related to this question
Learn chapter
Regression and Classification
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
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