Supervised vs. Unsupervised Learning: What's the Difference?

Supervised and unsupervised learning are two related types of machine learning. Here's everything you need to know about supervised vs. unsupervised learning, including how they relate, how they differ, as well as the advantages and limitations of each.

Overall Findings

Artificial intelligence (AI) programs rely on machine learning algorithms to perform novel tasks as they take in new information. Supervised learning uses sets of data labeled by a human to train AI. In unsupervised learning, the AI is fed raw, unlabeled data, which it must sort through on its own to identify patterns.

Supervised learning requires more human labor since someone (the supervisor) must label the training data and test the algorithm. Thus, there's a higher risk of human error,

Unsupervised learning takes more computing power and time but is still less expensive than supervised learning since minimal human involvement is needed. That said, it can be difficult for humans to verify the accuracy of outputs.

Both approaches are sometimes used together. For example, in semi-supervised learning, the initial training set includes labeled and unlabeled data.

Supervised learning and unsupervised algorithms can be combined with neural networks to achieve deep learning, or the ability to independently learn and make decisions.

Supervised Learning Pros and Cons

In supervised learning, an AI algorithm is first given training data (inputs) with clear labels (outputs). The AI then learns how to label future inputs of unlabeled data from the training set. For example, if you wanted to train an AI to categorize shapes, you'd begin by showing labeled pictures of circles, squares, triangles, etc.

Supervised learning is best used when you know what inputs and outputs to expect. When presented with an unlabeled shape that it previously hasn't seen, the AI wouldn't know what to do with it, so you need a lot of accurately labeled data to get the desired results. The training data must be diverse and different enough from the test data to ensure it will work in a real-world setting.

Supervised learning is used to train AI to recognize speech, handwriting, and objects and detect fraud and spam. Other practical uses include geographic mapping, news curation, marketing, and predicting real estate values.

Unsupervised Learning Pros and Cons

Unsupervised learning algorithms look for patterns in sets of unlabeled data. These AI algorithms learn by comparing the similarities and differences between different data points.

For example, if given unlabeled images of different shapes, an unsupervised learning algorithm might sort them by size or color. Then, it might get more specific, such as sorting shapes based on their number of sides.

The outputs of unsupervised learning can be hard to predict, and verifying their accuracy can also prove difficult. If the algorithm gets too specific or not specific enough, it can result in too many or too few categories to be helpful.

Unsupervised learning is most useful when you have a lot of raw data that would take a long time for a human to sort through and analyze. This approach to AI has practical applications in cybersecurity, computer vision, quality assurance, and even healthcare.

When to Use Supervised vs Unsupervised Learning

Supervised learning works best when you have a clearly defined problem for which you know the possible outcomes. For instance, supervised learning algorithms are good at detecting spam because there are only two possibilities: A message is spam or not. The same goes for predicting the temperature, prices, or inventory needs since you know the output will be a number.

Unsupervised learning algorithms are useful when you don't know what outputs to expect. Detecting abnormalities for quality assurance is a good example since you can't predict abnormalities. Another example is recommendation engines for streaming services since new content is always added.

Semi-supervised learning is a good compromise when you have a clearly defined problem but have limited labeled data to work with.