What Is Classification and When Do You Use It

How Classification Works

A classification model takes a row of input data (like customer age, purchase frequency, and support tickets filed) and returns a category label (like "will churn" or "will stay"). The model learns the relationship between input features and output labels by studying hundreds or thousands of examples where you already know the correct answer.

For example, you might have a spreadsheet of 2,000 past customers. Each row has 10 columns of customer data plus a final column showing whether that customer cancelled within 90 days. You train a classifier on this data, and it figures out which combinations of features are most predictive of cancellation. When you feed it a new customer record without the outcome column, it predicts the most likely outcome based on what it learned.

Binary vs Multi-Class Classification

Binary classification predicts one of two outcomes: yes or no, spam or not spam, churn or stay, fraud or legitimate. This is the most common type and often the easiest to train because the model only needs to draw one dividing line in the data.

Multi-class classification predicts one of three or more categories: high/medium/low priority, product category A/B/C/D, or customer segment 1/2/3/4/5. Multi-class is more demanding because the model needs to learn the boundaries between multiple groups, but it handles the added complexity well when you have enough training data for each category.

Available Classification Algorithms

The Data Aggregator app offers several classification algorithms. Each has strengths for different types of data:

• Decision tree: Easy to understand, works well with mixed data types. Good starting point for most problems. Shows you which features mattered most in the decision.
• Random forest: Builds many decision trees and combines their votes. More accurate than a single tree, handles noisy data better, and resists overfitting. One of the most reliable general-purpose classifiers.
• Gradient boosting: Builds trees sequentially, with each new tree correcting the mistakes of the previous ones. Often the most accurate option but takes longer to train. Best for large datasets where maximum accuracy matters.
• Logistic regression: Simple and fast. Works best when the relationship between features and outcome is roughly linear. Returns probability scores (like 73% chance of churn), which makes it useful when you need confidence levels, not just labels.
• Naive Bayes: Very fast to train, works well with text-based features and high-dimensional data. Good for spam detection and content categorization. Assumes features are independent, which is rarely true but works surprisingly well in practice.
• K-nearest neighbors: Classifies new data by finding the most similar records in the training data and using their labels. No real training phase, so it is fast to set up. Slower at prediction time for large datasets.
• Support vector machine: Finds the optimal boundary between classes. Works well in high-dimensional spaces and with smaller datasets. Most effective for binary classification problems.

If you are not sure which algorithm to pick, start with random forest. It works well on most classification problems without much tuning. See How to Choose the Right Algorithm for more guidance.

Real Business Examples

Customer Churn Prediction

Train a classifier on past customer data with a "cancelled" column. Features might include account age, monthly spend trend, support ticket count, login frequency, and product usage metrics. The model learns which patterns precede cancellation and scores current customers by churn risk. Your team can then focus retention efforts on the highest-risk accounts. See How to Predict Customer Churn Without Coding.

Lead Scoring

Train a classifier on past leads with a "converted" column. Features might include lead source, company size, industry, number of page views, and time spent on pricing pages. The model scores incoming leads as high, medium, or low probability of conversion. Sales reps prioritize their calls based on the score. See How to Score Leads With Machine Learning.

Fraud Detection

Train a classifier on past transactions with a "fraudulent" column. Features include transaction amount, time of day, geographic location, device type, and velocity (how many transactions in the last hour). The model flags suspicious transactions in real time. Because predictions cost zero credits, you can check every single transaction without adding per-check costs. See How to Detect Fraud With Anomaly Detection.

Support Ticket Routing

Train a classifier on past support tickets with a "department" column. Features might include keywords extracted from the ticket text, product area, customer tier, and urgency indicators. New tickets are automatically classified and routed to the right team, reducing manual triage time.

Classification vs Regression

Classification predicts a category (churn/stay, spam/not spam, high/medium/low). Regression predicts a number (revenue amount, days until next purchase, estimated price). If your question has a finite set of possible answers, use classification. If the answer could be any number, use regression.

Sometimes the same problem can be framed either way. "Will this customer spend more than $100 next month?" is classification (yes/no). "How much will this customer spend next month?" is regression (a dollar amount). Choose whichever framing is more useful for your business decision.