Unsupervised vs. supervised machine learning
Valérie Bécaert Valérie Bécaert
October 11 5 min

Unsupervised vs. supervised machine learning

Within the field of artificial intelligence, there are many approaches to machine learning. These can be broadly broken down into several subcategories, two of which are: supervised and unsupervised machine learning. Let’s explore the difference between them and consider what makes each most suitable for particular tasks and business cases.

Learning with examples

All varieties of machine learning analyze data, gain insights from it, and use this knowledge to make better, more informed decisions. The thing that distinguishes supervised machine learning, as the name suggests, is that it needs guidance in its learning process. In short, it needs to be taught.

This supervision comes in the form of structured data. Scientists use this data, which has already been labelled, to teach the algorithm. Imagine that each time the AI algorithm analyses a data point, it’s like a question in a test, with a right or wrong answer.

For instance, a scientist may train an algorithm for an image recognition task that decides whether a series of images portray oranges. The supervisor will already have labelled the training data with the correct answers, which allows the algorithm to see a picture of an orange and learn its fundamental features: for instance, round in shape and orange in colour.

Once the AI has been trained using this data, it can make its own decisions with new unlabeled data. If instead of a picture of an orange, it receives a picture of a banana as an input, it will recognize the differences between that and what it was trained to see.

Supervised machine learning in action

Supervised learning has many applications, and is much more commonly used than unsupervised learning. A good example of supervised learning is AI-powered machine translation.

First, scientists train the AI model on data drawn from existing books and text that have been translated. This provides the pre-existing connections between different languages. Then, after it reaches a certain threshold of accuracy, the model can be used to translate text it hasn’t seen before.

As well as classification tasks, supervised machine learning is also useful for data processing tasks like regression analysis; involving information that is presented on a continuous spectrum rather than a series of fixed points. Such types of data are very often collected by Internet of Things sensors.

Regression deals with predicting one value or factor in a system, such as a stock price or item weight, while taking into account a set of other values. Manufacturers, for example, can use AI-powered regression to predict the lifespan of production machinery, using the many data points provided by the machine. They can anticipate component or machine failure and schedule maintenance or part replacements, avoiding costly downtime from an unexpected failure.

One major challenge for supervised learning is that it relies on pre-existing datasets. In some cases, such data will not be available, or the challenge of labelling it would be beyond the ability of any person or group of people. In other cases, it’s the labelling itself that is what we want the AI model to do — for example, selecting and organizing the pictures of birds out of every photo on the Internet.

The self-taught machine

Unsupervised machine learning doesn’t need examples to learn. It takes unstructured (unlabelled) data and makes discoveries that can be used to form wider judgements, usually through clustering and association. The colloquial idea of artificial intelligence, of machines being able to analyze and interpret any new data they come across, is closest to unsupervised learning, although we’re years away from that point.

This makes unsupervised machine learning perfect for types of data analytics tasks that aren’t looking for the right answer to a question, such as “banana or orange?” Instead, they’re searching for useful patterns and anomalies in data. These insights can result in business optimization and innovations that aren’t available through any other method.

Applications of unsupervised ML

The insurance industry is all about managing risk. Techniques such as clustering (grouping and sorting raw data), anomaly detection and association mining are used in insurance to discover unexpected connections and minimize risk by grouping demographics together. Anomaly detection can aid in identifying outliers and possible instances of fraud.

Another example is retailers, who can use unsupervised machine learning to enhance their customer segmentation, discovering patterns in customers’ spending data that can be used to refine their marketing efforts and even their business models. As the quantity and depth of data available increases, the kind of insights machine learning can deliver will only grow.

While unsupervised learning can be very powerful, it has many drawbacks. Right now, it can use huge amounts of computing power, and its outputs are not always useful. It can provide spurious correlations and patterns within data just as easily as it can provide innovative insights. For many applications, the narrow focus of supervised learning is superior for getting results.

Finding the right technology for your needs

You may find your organization could benefit most from supervised machine learning, or that unsupervised is the way to go – or a combination of the two. Artificial intelligence is advancing constantly, so perhaps these terms will be superseded as we learn how to make AI models work in newer, smarter ways. You can read more about all things AI right here.