Machine learning (ML) powers many technologies that we rely on daily, such as image recognition and autonomous vehicles. Two foundational approaches—supervised and unsupervised learning—form the backbone of these systems. While both are key to training ML models, they differ in their methodology, goals, and applications.
In this guide, we’ll compare these two approaches, highlight their differences, and explore their benefits and challenges. We’ll also explore practical applications to help you understand which is best suited for various tasks.
Table of contents
- What is supervised learning?
- What is unsupervised learning?
- Supervised vs. unsupervised: key differences
- Benefits of supervised and unsupervised learning
- Challenges of supervised and unsupervised learning
- Applications of supervised and unsupervised learning
- Conclusion
What is supervised learning?
Supervised learning trains ML systems using labeled data. In this context, “labeled” means that each training example is paired with a known output. These labels, often created by experts, help the system learn the relationships between inputs and outputs. Once trained, supervised systems can apply these learned relationships to new, unseen data to make predictions or classifications.
For instance, in the context of self-driving cars, a supervised learning system might analyze labeled video data. These annotations identify street signs, pedestrians, and obstacles, enabling the system to recognize and respond to similar features in real-world driving scenarios.
Supervised learning algorithms fall into two primary categories:
- Classification: These algorithms assign labels to new data, such as identifying emails as spam or non-spam.
- Regression: These algorithms predict continuous values, like forecasting future sales based on past performance.
As datasets grow and computational resources improve, supervised systems become more accurate and effective, supporting applications such as fraud detection and medical diagnostics.
What is unsupervised learning?
Unsupervised learning, by contrast, analyzes data without labeled examples, relying on statistical algorithms to uncover hidden patterns or relationships. Unlike supervised systems, these models infer structure and update their findings dynamically as new information becomes available. While unsupervised learning excels at pattern discovery, it is typically less effective for predictive tasks.
A practical example is news aggregation services. These systems group related articles and social media posts about a breaking news event without external labeling. By identifying commonalities in real time, they perform unsupervised learning to highlight key stories.
Here are a few specialized unsupervised learning algorithms:
- Clustering: These are used to segment consumers and adjust segments as behaviors change.
- Association: These detect patterns in data, such as identifying anomalies that could indicate security breaches.
- Dimensionality reduction: These simplify data structures while preserving critical information and are often used in compressing and visualizing complex datasets.
Unsupervised learning is integral to exploratory data analysis and uncovering insights in scenarios where labeled data is unavailable.
Supervised vs unsupervised: key differences
Supervised and unsupervised learning serve distinct roles in ML. These approaches differ in data requirements, human involvement, tasks, and applications. The table below highlights these differences, which we’ll explore further.
| Supervised learning | Unsupervised learning | |
| Input data | Requires labeled data | Requires unlabeled data |
| Objective | Predict or classify output labels based on input features | Discover and update hidden patterns, structures, or representations in data |
| Human involvement | Significant manual effort for labeling large datasets and expert guidance for choosing features | Minimal but very specialized human intervention. Primarily for setting algorithm parameters, optimizing resource use at scale, and algorithm research. |
| Primary tasks | Regression, classification | Clustering, association, dimensionality reduction |
| Common algorithms | Linear and logistic regression, decision trees, neural networks | K-means clustering, principal component analysis (PCA), autoencoders |
| Output | Predictive models that can classify or regress new data points | Groupings or representations of the data (e.g., clusters, components) |
| Applications | Spam detection, fraud detection, image classification, price prediction, etc. | Customer segmentation, market basket analysis, anomaly detection, etc. |
Differences during the training phase
The primary difference between the two types of algorithms is the type of datasets they depend on. Supervised learning benefits from large sets of labeled data. Consequently, the most advanced supervised systems depend on large-scale, unspecialized human labor to sift through data and generate labels. Labeled data is also usually more resource intensive to process, so supervised systems can’t process as much data at the upper end of the scale.
Unsupervised learning systems can start to be effective with smaller datasets and can process much larger amounts of data with the same resources. Their data is easier to obtain and process since it doesn’t depend on large-scale, unspecialized human labor. As a trade-off, the systems don’t usually achieve as high a degree of accuracy on prediction tasks and often depend on specialized work to become effective. Instead of being used where accuracy is crucial, they are more frequently used to infer and update patterns in data, at scale, and as data changes.
Differences when deployed
Supervised learning applications usually have a built-in mechanism to obtain more labeled data at scale. For example, it’s easy for email users to mark whether incoming messages are spam or not. An email provider can accumulate the marked messages into a training set and then train logistic regression systems for spam detection. They trade off longer and more resource-intensive training for faster decision-making when deployed. Besides logistic regression systems, other common supervised training algorithms include decision trees and neural networks, which are used ubiquitously to predict and make decisions and for complex pattern recognition.
Unsupervised systems distinguish themselves when applied to problems involving large amounts of unstructured data. They can detect patterns in the data, even when they are transient, and must be detected before training for supervised learning is complete. For example, clustering algorithms, a type of unsupervised learning system, can detect and update consumer segments as trends shift. If trends shift to new and unseen patterns, they remain relevant without requiring downtime for retraining.
An example of unsupervised learning is the use of principal component analysis (PCA) in finance. PCA is an algorithm that can be applied to groups of investments at scale and helps infer and update emergent properties of the group. These include important financial indicators, such as the most important sources of investment risk and factors likely to impact returns. Other common types of unsupervised learning systems are autoencoders, which compress and simplify data, often as a preparatory step before other ML algorithms are applied.
Benefits of supervised and unsupervised learning
Both supervised and unsupervised systems are useful for processing data at a scale and speed that surpass that of unaided humans. However, they are best suited for different applications. Below, we contrast some of their main benefits.
Supervised systems
- Excel when there is significant historical data available
- Are much better than unsupervised systems for training data with known structure, characteristics, and patterns
- Are ideal for detecting and applying known characteristics of data at scale
- Can produce results that are understood and make intuitive sense for humans
- Can have higher accuracy on new and unseen data
- Can make predictions more quickly and at a higher scale than unsupervised systems
Unsupervised systems
- Are particularly good at identifying previously unseen or unknown structures and relationships in data
- Do well when the data is less structured and its properties are less well known
- Work in some conditions where supervised systems don’t work well (for example, in situations where data is not available or where it is available but hasn’t been processed by humans)
- Require fewer resources and less time during training than supervised systems for equivalent amounts of data
- Can be trained and used when there is too much data to process well with supervised systems
Challenges of supervised and unsupervised learning
Supervised and unsupervised systems each make different trade-offs, and the challenges they face are sometimes quite different. We highlight some of the main differences below.
Supervised systems
- Require access to large amounts of human-processed data, which is only sometimes available or easy to obtain
- Often have longer and more resource-intensive training phases
- May struggle to adapt quickly if core data characteristics change
- Face challenges when processing inherently unstructured data, such as video or audio
Unsupervised systems
- Will more frequently detect patterns that don’t generalize well to new data examples
- Can be difficult to make as accurate as supervised systems
- They produce results that are difficult for humans to interpret, and the interpretations of these results can be more subjective.
- Can take more time and resources per prediction made in the real world
Applications of supervised and unsupervised learning
Some applications and problems are best addressed with supervised learning systems, some are best with unsupervised systems, and some do best using a blend. Here are three well-known examples.
Mixed learning systems and semi-supervised learning
It’s important to note that most real-life applications use a mix of supervised and unsupervised models. Learning systems are often combined based on things like budget, data availability, performance requirements, and engineering complexity. Occasionally, a specialized subset of learning algorithms that attempts to blend the benefits of both approaches—semi-supervised learning—might also be used. In the examples below, we call out the most likely or primary system that’s likely to be used.
Traffic prediction (supervised)
Traffic prediction is a challenging task. Fortunately, a lot of labeled data is available since cities regularly audit and record road traffic volumes. Regression algorithms, a type of supervised learning, are easy to apply to this data and can produce quite accurate predictions of traffic flows. Their predictions can help inform decision-making around road building, traffic signage, and placement of traffic lights. Unsupervised algorithms are less effective at this phase. They can, however, be run on traffic data as it accumulates after a change in road structure is implemented. At that point, they help automatically identify and infer if any new and previously unseen problems might occur.
Genetic clustering (unsupervised)
Analysis of genetic data can be slow and cumbersome since the volumes of data are large and most of the data isn’t well analyzed. We often don’t know much about what the genetic data contains—where genes and other genetic components might be stored in the genome, how they are decoded and interpreted, etc. Unsupervised algorithms are particularly relevant to this problem since they can process large amounts of data and automatically infer what patterns it contains. They can also help collect similar-looking genetic information into separate clusters. Once genetic data is clustered based on similarity, the clusters can be easily processed and tested to identify what biological function (if any) they serve.
LLMs and reinforcement learning (mixed)
Large language models (LLMs) are an example of an application that combines unsupervised and supervised learning systems. The initial system, the LLM, is usually an example of an unsupervised system. To produce an LLM, large-scale data are analyzed (say, all the English language text available on the internet) by an unsupervised system. The system infers many patterns from the data and develops basic rules for conversing in English.
However, the inferences an LLM makes don’t do a good job of helping it sound like a typical human in conversation. They also don’t help it take into account individual preferences for communication. A supervised system—specifically, a reinforcement system that uses annotated feedback from users (called reinforcement learning from human feedback, or RLHF for short)—is one way to solve this problem. RLHF can be applied to an already-trained LLM to help it speak well with humans in general. It can also learn individual preferences and speak in ways a specific person prefers.
Conclusion
In summary, supervised and unsupervised learning are two fundamental subsets of ML, each offering unique strengths. Supervised learning excels in scenarios with abundant labeled data, sufficient resources for up-front training, and a need for rapid, scalable decision-making. On the other hand, unsupervised learning shines when uncovering hidden structures and relationships in data, especially when labeled data or training resources are limited and decision-making can accommodate more time and complexity. By understanding the advantages, challenges, and use cases of both approaches, you can make informed decisions about when and how to apply them effectively.
