Classification vs Regression: Predicting Categories vs Numbers

In the previous post, we explored the distinction between supervised and unsupervised learning. Today, we’re exploring fundamental mental models for machine learning: understanding classification and regression.

Classification and regression in machine learning form the foundation of how supervised learning algorithms make predictions. Understanding when to apply each method is key to developing effective models that accurately address your specific task.

The Core Difference: Categories vs Numbers

The simplest way to understand the classification versus regression divide is to ask: “Are you trying to predict what will happen, or how much will happen?” That simple question cuts to the heart of the classification versus regression:

Classification answers “which category?” questions. It predicts a label or category from a set of possibilities. Will this customer churn (stop using our service)? Is this email spam? Which product category (electronics, clothing, groceries) will this shopper buy next? The key here is that we’re sorting things into distinct buckets or groups.

Regression answers “how much?” questions. It predicts a numerical value along a continuous spectrum. How much will this house sell for? What will our sales volume be next quarter? How many minutes will this customer stay on the phone? Here we’re finding a specific point on a number line rather than assigning to a category.

Why This Distinction Matters

You might wonder why we need separate approaches for these different prediction types. Why not just use one algorithm for everything? Well, it all comes down to how these models figure out if they’re doing a good job and how they adjust their parameters (the numerical values that define the model’s behavior) during training.

Classification models try their best to get the category right. They learn where to draw the lines between different groups. Getting a prediction “almost right” just doesn’t cut it. Think about it. An email can’t be “kind of spam” in any meaningful way. A tumor can’t be “somewhat malignant” for medical decisions. It’s one or the other.

Regression models predict numbers rather than categories. They aim to get as close as possible to the correct value. For example, when predicting house prices, being off by $1,000 (predicting $299,000 when the actual price is $300,000) is much better than being off by $50,000. It’s like guessing someone’s age – being off by 1 year is better than being off by 10 years. During training, the model learns which factors (like square footage or neighborhood) affect the price most, adjusting how much weight it gives each factor to make more accurate predictions.

Because classification and regression solve different types of problems, they use different methods to make predictions, different ways to measure success, and different strategies to improve their accuracy.

Classification: Predicting Categories

Classification is all about teaching computers to sort things into categories. Imagine drawing lines between different groups based on their features. For example, with animals, the computer learns which combinations of weight, height, and fur length typically distinguish cats from dogs from rabbits.

In machine learning, the categories we assign are called “labels” – they’re the answers we want our model to predict. Labels are simply the names of the groups or classes that items belong to. Types of classification tasks include:

Binary Classification: Tasks with exactly two possible labels

• Email: Spam or not spam
• Loan application: Approve or deny
• Medical test: Positive or negative

Multi-class Classification: Tasks with three or more possible labels where each item belongs to exactly one class

• Product categorization: Electronics, clothing, home goods, etc.
• Customer segmentation: Budget-conscious, quality-focused, convenience-seeking
• Document classification: Finance, HR, Technical, Marketing

Multi-label Classification: Tasks where items can belong to multiple categories simultaneously

• Movie genres: A film can be both “action” and “comedy”
• Medical diagnoses: A patient can have multiple conditions
• Content tagging: An article might be labeled with “AI,” “business,” and “ethics”

The key difference between multi-class and multi-label classification is that in multi-class, each item gets assigned exactly one label from a set of options, while in multi-label, an item can receive multiple labels at the same time.

Regression: Predicting Numerical Values

Regression analyzes relationships between variables to predict numerical values. Before diving in, let’s clarify two key terms:

• Features: The input variables or characteristics we use to make predictions (like square footage or location)
• Target: The numerical value we’re trying to predict (like a house price)

A classic example is house price prediction, where features like square footage, neighborhood, and number of bedrooms contribute to predicting a specific dollar amount as the target. Common regression types include:

Simple Linear Regression: Predicting a numerical target using a single feature

• Predicting height based on age for children
• Estimating crop yield based on rainfall
• Forecasting sales based on advertising spend

Multiple Regression: Using multiple features to predict a numerical target

• House price prediction using location, size, age, etc.
• Salary estimation based on experience, education, industry, and location
• Energy consumption forecasting using temperature, time of day, season, and building occupancy

Polynomial Regression: Capturing curved (non-linear) relationships between features and targets

• Modeling plant growth over time (which follows a curve, not a straight line)
• Predicting performance degradation in machinery
• Estimating drug response based on dosage

When we say “polynomial” here, we’re simply acknowledging that the relationship between our input and output might be curved rather than a straight line. Think about how a plant grows quickly at first, then levels off as it matures. That relationship isn’t a straight line, so we need a model that can capture those curves.

Real World Examples That Make This Concrete

To better understand when to use classification versus regression, consider these real-world applications. Here are some examples that highlight when to use each approach:

Classification Examples:

• Fraud detection: Is this transaction fraudulent? (binary)
• Language identification: Which language is this text written in? (multi class)
• Image recognition: What objects appear in this photo? (multi label)
• Sentiment analysis: Is this review positive, negative, or neutral? (multi class)

Regression Examples:

• Temperature forecasting: What will tomorrow’s high temperature be?
• Stock price prediction: What will this stock be worth next quarter?
• Resource allocation: How many server instances will we need next week?
• Customer lifetime value: How much revenue will this customer generate?

Both approaches have their place, and real-world applications often integrate them – such as in healthcare platforms that first classify patients into risk groups and then predict their specific treatment costs.

How to Recognize Which Approach You Need

Not sure which approach fits your problem? Here’s a practical way to decide.

Use classification when: Your output represents categories or states. Think about customer segments or product types. The boundaries between outcomes need to be clear-cut. You’re basically answering “which type?” or “yes/no” questions. For example, “Is this email spam?” has only two possible answers.

Use regression when: You need a number on a continuous scale. The actual numerical value matters, not just the category it falls into. These problems usually answer “how much?” or “how many?” questions. Like “What price will this house sell for?” where $250,000 is meaningfully different from $275,000.

The choice isn’t always obvious. Take a grocery chain trying to predict “customer value.” They could approach this as regression by forecasting exact spending amounts. Or they might use classification by grouping customers into value tiers like “premium,” “regular,” and “occasional.” What works best depends on what they plan to do with this information. For instance, if they’re designing a loyalty program with three distinct tiers of rewards, classification makes more sense. But if they’re optimizing inventory levels based on expected revenue, knowing the exact predicted spending amounts through regression would be more useful.

Where Each Approach Shines

Both classification and regression have their sweet spots in different situations.

Classification works best when decisions need clear categories. A loan application is approved or rejected, not “kinda approved.” It’s perfect for automating decision processes where you need definite answers.

I’ve found that classification particularly helps teams when they need to sort items into buckets for different treatments or workflows. For instance, support tickets get routed to different teams based on their classification.

Regression, on the other hand, excels with precise numerical predictions. It helps you understand how changing one thing affects another in proportional ways. Need to forecast next quarter’s sales? Regression. Want to know exactly how much to price a product? Also regression.

These approaches often work together across many industries. In ecommerce, classification helps predict which product categories a customer will browse (so you can customize their navigation experience), while regression predicts their likely spend amount (allowing you to tailor discount offers that maximize profit while feeling generous to the customer). Similarly, in healthcare, doctors might use classification to determine which treatment protocol a patient needs, then use regression to predict their recovery timeline. Financial services firms classify customers by investment strategy preference, then use regression to forecast potential returns for their specific portfolio mix.

Common Pitfalls and Misconceptions

Ever tried to force a round peg into a square hole? That’s what happens with these common mistakes:

Pitfall 1: Turning continuous problems into categories It’s tempting to simplify a continuous measurement like “customer spending” into buckets like “low,” “medium,” and “high.” But this throws away valuable information! I once saw a marketing team create spending tiers only to realize they couldn’t tell if someone was barely in a tier or almost in the next one up. If your variable is naturally continuous, regression usually works better.

Pitfall 2: Using regression for yes/no outcomes Customer churn seems like it could be a regression problem – predicting the probability someone leaves. But since customers ultimately either stay or go, classification models that are built specifically for these binary outcomes typically perform better.

Pitfall 3: Creating too many categories More isn’t always better. I’ve watched retail analysts start with three customer categories, then expand to seven, then twelve – only to find their teams couldn’t meaningfully use that many distinctions. Four well-defined shopper categories often provide more actionable insights than a dozen overly specific ones.

Pitfall 4: Mishandling ordered categories Survey responses like “Very Dissatisfied” to “Very Satisfied” aren’t truly numerical, even though there’s an order to them. These are ordinal values – they have a clear ranking, but the distance between categories isn’t equal. The jump from “Neutral” to “Satisfied” might represent a smaller change in customer feeling than the jump from “Satisfied” to “Very Satisfied.” Treating these as simple numbers can result in misleading results.

Bringing It All Together

This classification versus regression distinction gives us a practical framework for tackling prediction tasks. Combined with what we know about supervised versus unsupervised learning, we now have a more complete toolkit:

• Supervised Learning (Classification): Predicting categories using labeled examples
• Supervised Learning (Regression): Predicting numerical values using labeled examples
• Unsupervised Learning: Finding patterns in data without predefined outputs

Each approach serves different needs. Many real-world systems combine these approaches, like recommendation engines that group similar products (unsupervised), predict whether you’ll like them (classification), and estimate how much you might spend (regression).

The real skill isn’t just knowing various algorithms—it’s recognizing which type of problem you’re facing and choosing the right approach. Sometimes the hardest part of the job is correctly framing the question.

In our next article, we’ll explore “Prediction vs Inference: Different Goals in ML Analysis,” examining whether your priority is making accurate predictions or understanding what drives your outcomes.

What machine learning challenges have you faced when deciding between classification and regression? I’d love to hear about your experiences in the comments below.