Supervised vs unsupervised learning: Which one is right for your business?

As businesses aim to make data-driven decisions, the use of machine learning (ML) has become increasingly prevalent. But when embarking on a machine learning project, one of the critical decisions you will face is choosing between supervised and unsupervised learning.

Knowing the difference between these two approaches and which is best suited for the needs of your business can make all the difference for tech leaders. ML can be the bedrock for powerful AI tools and complement business AI implementation – but only if it’s paired with the right data.

Supervised and unsupervised learning differences

At the core of machine learning lies data—specifically how that data is used to train algorithms. You can learn more about specific algorithms used in supervised and unsupervised learning in our algorithm explainer. Supervised and unsupervised learning differ significantly in the types of data they rely upon.

Supervised learning uses, structured, labeled data to train models. Labeled data means that each data point in the dataset is associated with the correct output. For instance, if you are using a supervised model to predict customer churn, each customer record (input) is labeled with whether the customer still uses your product or not (output).

The key advantage of supervised learning is that it provides a clear understanding of the relationship between inputs and outputs. This makes it particularly well-suited for tasks such as classification, in which data is assigned a specific category and regression. The latter is when a relationship is established between the input and output data, where the model needs to predict specific outcomes or place data into predefined categories. Algorithms such as decision trees, support vector machines, and linear regression models are commonly used for supervised learning tasks.

In contrast, unsupervised learning works with unlabeled data. The algorithm doesn’t have access to a set of predefined labels and instead attempts to identify patterns, structures, or relationships within the data. This is especially useful for exploratory data analytics when you don’t know exactly what you’re looking for in the data.

Common techniques in unsupervised learning include clustering, association, and dimensionality reduction. For example, clustering algorithms like K-means help businesses group similar customers together based on behavior or characteristics, without needing prior labels for these groups. Association algorithms, like those used in recommendation engines, can reveal connections between products customers tend to purchase together.

Supervised and unsupervised learning examples

The use cases for supervised and unsupervised learning vary depending on the problem you’re trying to solve, as well as the type of data you have access to.

Supervised learning use cases include:

• Forecasting: Supervised learning is often used to forecast future outcomes based on historical data. In the financial sector, for instance, supervised learning models predict stock prices, credit risks, or future sales trends.
• Classification Tasks: Supervised learning excels at classification tasks. For example, an email provider might use a supervised model to classify incoming messages as spam or phishing attempts.
• Sentiment Analysis: Supervised models are used to classify text into different categories, such as positive or negative sentiment, which is helpful for customer feedback analysis.
• Predictive Maintenance: In manufacturing, supervised learning can predict when machinery is likely to fail, allowing businesses to schedule maintenance in advance. This can be crucial in smart manufacturing environments or smart ports, which often use Internet of Things (IoT) devices to collect and send data on the condition of equipment.

“Supervised learning is proficient in classification and prediction, such as in credit risk monitoring or image processing,” explains Edward Challis, head of AI strategy at UiPath. However, it requires a large amount of accurately labeled data, which can be costly and time-consuming to collect.”​

Unsupervised learning use cases include:

• Customer Segmentation: Unsupervised learning is ideal for grouping customers based on behavior, such as purchase habits or website interactions. This segmentation can drive more personalized marketing strategies.
• Anomaly Detection: In industries such as finance and cybersecurity, unsupervised models can be used to detect unusual behavior, such as fraudulent transactions or security breaches.
• Recommendation Engines: By analyzing patterns in user behavior, unsupervised learning can power recommendation systems, suggesting products or services that users are likely to be interested in.
• Market Basket Analysis: Unsupervised models can reveal associations between items frequently purchased together, which is helpful for retailers looking to optimize their store layouts or online recommendation systems.

Vitor Monteiro, CTO at Unflow, notes that “unsupervised learning techniques like clustering and anomaly detection help businesses uncover hidden patterns in their data, allowing for discoveries that might not have been apparent otherwise.”​

Which kind of machine learning is best for your business?

Choosing between supervised and unsupervised learning depends largely on your business goals, the data available, and the specific outcomes you aim to achieve.

When to choose supervised learning:

• You have labeled data. If your business has access to a large dataset where both inputs and outputs are clearly labeled, supervised learning is often the better choice. For instance, if you have historical data on customer churn, supervised learning can predict which current customers are likely to leave.
• You need specific predictions. Supervised learning is ideal when your business requires specific, actionable insights, such as predicting future sales, classifying emails, or determining whether a customer will respond to a marketing campaign.
• You want higher accuracy. Supervised models tend to be more accurate than unsupervised models because they are trained on specific, labeled examples. This is crucial in fields like healthcare, where precision is critical.

However, there are some challenges. “Obtaining labeled data is the key challenge in supervised learning, especially ensuring that the samples are representative of the broader context,” says ​Peter van der Putten, head of the AI Lab at Pegasystems. He explains that this is one of the main obstacles businesses face in this field, as it can be costly and time-consuming.

When to choose unsupervised learning:

• You lack labeled data. If your business doesn’t have labeled data—or it would be expensive or difficult to obtain—unsupervised learning is a better option. This is particularly relevant for businesses just beginning to explore their data.
• You want to explore the data. Unsupervised learning is excellent for discovering hidden patterns in data. For example, if you want to understand the different customer segments in your business, an unsupervised model can reveal these groups based on behavior.
• You are focused on insights, not predictions. If your goal is to gain insights rather than make specific predictions, unsupervised learning is ideal. For example, it’s useful for identifying patterns in customer behavior or detecting anomalies in network traffic.

Dr. Challis adds, “Unsupervised methods like clustering can lead to interesting insights, but they are harder to operationalize compared to the more actionable predictions from supervised learning.”​

Hybrid approaches: The best of both worlds?

In some cases, a hybrid approach to machine learning may be the best option for your business. Hybrid or semi-supervised learning combines elements of both supervised and unsupervised learning, allowing businesses to use a small amount of labeled data to guide the learning process while still leveraging the vast amounts of unlabeled data they have access to.

“Hybrid approaches like semi-supervised learning can improve performance in cases where labeled data is scarce, such as using unsupervised pre-training on unlabeled data before fine-tuning with labeled examples,” notes Monteiro.

Ultimately, the decision between supervised and unsupervised machine learning comes down to your business objectives, the data you have at hand, and the level of accuracy and insight you require. By aligning the machine learning method with your business goals, you can unlock powerful insights, whether you’re predicting future trends or uncovering new customer segments.