Supervised and unsupervised methods – how to choose the right approach for a business problem?

Supervised learning – when we know the answer

In supervised tasks, we deal with situations where, for each observation, we know the outcome we want to predict – the so-called label. This can be a specific numeric value (e.g., income, age) or a category (e.g., yes/no, sick/healthy, purchased/did not purchase).

The model learns from these known examples by analyzing which features (input variables) lead to specific outcomes. After learning these patterns, it can predict outcomes for new, unknown cases.

It’s a bit like studying with answer keys – first, we show the student tasks with solutions, and then we test them on new questions.

Typical supervised learning tasks:

• Regression – when we want to predict a numerical value.

Examples:

• • Predicting a house price based on area, location, and construction year,
  • Estimating a patient’s hospital stay duration,
  • Forecasting sales in the next quarter.

• Classification – when we want to assign a new case to one of several predefined categories.

Examples:

• • Determining whether a customer will churn,
  • Identifying whether a message is spam or not,
  • Diagnosing whether a patient has a specific disease.

Practical example:

You’re an analyst at a company offering online subscriptions (e.g., a streaming service). You have data on customer behaviors:

• Number of logins per week,
• Time spent watching content,
• Categories of interest,
• Duration of subscription.

You also know whether a customer canceled their subscription in the past 3 months – this is your label, the value you want to predict. The goal is to build a model that predicts churn risk based on behavior.

This allows you to:

• Identify at-risk customers early,
• Launch retention actions (e.g., personalized offers, discounts, reminders),
• Optimize marketing efforts.

This is a classic classification task – you have historical data with known “answers” and the model learns to recognize churn patterns. The result? You can make proactive business decisions instead of reacting only after the customer has left.

Unsupervised learning – when we look for structure

In unsupervised tasks, we don’t have a known answer the model should learn. We don’t know in advance what the “right” categories, groups or patterns are – we want to discover them. Our goal is to understand the structure of the data, find similarities, detect anomalies or simplify information. It’s like observing a crowd and trying to figure out who resembles whom, even though no one wears a label like “athlete” or “parent with child”. We analyze the data and try to naturally group it in a meaningful way.

Typical unsupervised learning tasks:

• Clustering – identifying groups of similar observations.

Examples:

• • Customer segmentation in marketing,
  • Grouping documents by topic,
  • Clustering genes by expression similarity.
• Dimensionality reduction – simplifying a large number of variables while preserving key information.

Examples:

• • Preparing data for visualization (e.g., PCA, t-SNE),
  • Removing noise or correlations between features.
• Anomaly detection – identifying observations that deviate from the norm.

Examples:

• • Detecting payment fraud,
  • Identifying measurement errors,
  • Spotting unusual user behaviors.

Practical example:

You have data about online store customers:

• Number of website visits,
• Time spent in the store,
• Number of items in the cart,
• Average order value.

You don’t know who is a loyal customer, who just browses, and who occasionally buys. Still, you want to create customer segments to tailor marketing communication and offers. This is a classic clustering task – unsupervised. Based on behavioral similarities, customers are automatically grouped into clusters such as “frequent buyers”, “browsers” and “occasional customers”. Segmentation allows you, for instance, to send newsletters only to active users or offer discounts to those who rarely return.

Since unsupervised learning lacks labels, it’s harder to assess “quality” directly. We often rely on business intuition, visualizations or internal metrics (e.g., silhouette score in clustering). The key to success is good data preparation – transforming variables, removing outliers, standardizing. Unsupervised tasks are also great for early data exploration when we don’t yet know what the data hides, but we want to extract initial insights.

• What if you don’t know the task type?Although the distinction between supervised and unsupervised tasks seems clear in theory, in practice, many analytical problems don’t fit neatly into one category. We may intuitively think “this is probably classification” or “this looks like segmentation” – but only deep understanding of the data and business goals leads to the right decision.Here are a few real-life examples showing why it’s crucial to ask: Do I really know what I want to predict? And do I have the data to make that possible?Customer review analysisAn e-commerce company receives hundreds of product reviews daily in free-text form, e.g.:
  • “Great product, but delivery took too long.”
  • “The package arrived damaged, but I quickly got a replacement – recommend!”
  • “Not recommended – looks different than in the pictures.”

  The customer experience team wants to:

  • Understand main topics and issues raised in the reviews – e.g., quality, delivery time, packaging, etc.
  • Identify “negative” reviews that may require intervention – e.g., customer service response or process improvement.

  At first glance – a typical opinion analysis. But is it supervised or unsupervised?

  If no review labels exist (e.g., positive/negative), we can’t start with classification. We begin with unsupervised methods – clustering or topic modeling – to uncover main themes. Only after some reviews are manually labeled can we move to supervised models that automatically classify new entries.

  This is a hybrid task: the type of analysis depends on the project phase and available data. It also shows that before choosing an algorithm, you need to deeply understand the data and the analysis objective.

  Why is this example tricky? Because “review sentiment” sounds like classification (supervised), but without labels and structure, a predictive model isn’t possible. First, we must explore the data – then decide on the right modeling approach.

  Detecting machine failures in manufacturing

  A manufacturing company monitors machine operations in real-time, collecting sensor data such as:

  • Engine temperature,
  • Vibration level,
  • Rotational speed,
  • Pressure,
  • Noise level,
  • And other diagnostics.

  The goal is to detect or anticipate failures early to avoid costly downtime. Operators report that machines sometimes “act strangely” but this doesn’t always lead to breakdowns. The database contains hundreds of thousands of measurements, but only a few are labeled as actual failures – if any.

  This sounds like classification: “Is the machine about to fail – yes or no?” But data reality suggests otherwise.

  • Supervised approach: Possible if we have many well-documented failure cases – i.e., we know exactly when failures happened. We can then build a classifier to predict future cases. The challenge? These data are rare, imbalanced, and hard to collect. Failures may have different causes and patterns – the model may not generalize well.
  • Unsupervised approach: Ideal when we want to detect “unusual” behavior without a failure label. The model learns what “normal” operation looks like and flags deviations, possibly warning about issues before they escalate.

  Ultimately, the unsupervised method is more effective – because failure data are rare and heterogeneous. Such models learn normal machine behavior and signal anomalies. A good example of a problem that sounds like classification but works better as anomaly detection. Once again, understanding the data and operational context is essential.

  Detecting suspicious e-commerce transactions

  An online marketplace wants to flag suspicious transactions possibly resulting from:

  • Fraud,
  • System errors,
  • Abuse (e.g., discount exploitation),
  • Unusual behavior (e.g., bulk purchases from a personal account).

  Available data include:

  • Cart value,
  • Number of items,
  • Purchase time,
  • IP vs. delivery address,
  • Payment method,
  • Customer purchase history.

  The goal is real-time detection of “weird” transactions to:

  • Flag them for review,
  • Temporarily hold them,
  • Route them to additional verification.

  Supervised or unsupervised? It depends – and that’s what makes this case so interesting (and tricky):

  • Supervised approach: Possible if we have historical labels indicating which transactions were fraudulent. We can then build a classifier (e.g., logistic regression, XGBoost) that learns from the past and predicts future fraud. Problem? Such data are rare and often incomplete – only a fraction of fraud is caught and labeled, and false positives/negatives are hard to manage. The model may learn overly narrow patterns or introduce bias.
  • Unsupervised approach: We use algorithms to identify transactions that significantly differ from the norm – no labels required. This is often more realistic at the start of a project, when fraud types are still unknown. Instead of predicting “fraud” we detect outliers – then investigate them manually or semi-automatically.

  Although “fraud detection” intuitively sounds like classification (supervised), in reality it often starts as an unsupervised task – especially early on, when we lack clear definitions or labeled data.

  Summary

  As the examples above show, effective data analysis does not begin with choosing an algorithm but with a deep understanding of the problem and careful translation into the language of data science. This early stage – often overlooked – has the greatest impact on the value and relevance of the final outcome.

  While the distinction between supervised and unsupervised learning might seem simple in theory, in practice it requires careful examination of data, business goals, and project constraints. Sometimes what looks like classification turns out to be clustering. Sometimes a predictive model is meaningless without first uncovering data structure.

  So before you choose tools and techniques, pause. Ask the right questions. Check if you have labels – or if you still need to discover them. Only then decide on the right approach. Because in data science – just like in good diagnostics – it’s not about finding an answer, but about answering the right question.

  Author: Anna Wilk, Head of Data Analysis Team at StatSoft