When should a classification model be used?

Use a classification model when the outcome you want to predict is a category or class rather than a continuous numerical value.

When is classification appropriate?

• The target is categorical: for example, Fraud / Not Fraud, Will Churn / Will Stay, or Low / Medium / High Risk.
• You have labeled examples: historical records include the correct class, allowing a supervised model to learn patterns.
• Input features contain useful signals: customer activity, transaction details, or message text help distinguish classes.
• The prediction supports a decision: for example, flag a suspicious payment or route a support ticket to the right team.

Practical example

Suppose a telecom company wants to identify customers likely to cancel their subscriptions.

• Input features: recent usage, complaints, plan type, and payment history.
• Target: Churn or No Churn.
• Output: the predicted class, often accompanied by an estimated probability of churn.

A probability threshold can be selected based on the cost of missing a customer who will churn versus unnecessarily contacting one who will stay.

When should you use something else?

• Regression: when the target is a quantity, such as monthly revenue or the number of units sold.
• Rule-based logic: when the categories can be determined reliably by simple, explicit rules without learning from examples.

Important: A class encoded as 0 or 1 is still categorical; numerical encoding alone does not make the task regression.

What should you check before choosing a classifier?

Ensure that labels are trustworthy, examples represent the cases the model will encounter, and the evaluation metric reflects the cost of different mistakes. Accuracy alone may be misleading for rare outcomes such as fraud.

In short, choose classification when you need to assign observations to predefined classes using patterns learned from labeled data.