Supervised, Unsupervised and Semi-Supervised Learning Explained

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Machine learning has become a popular topic that feeds people’s fascination as they marvel at what computer algorithms can do after proper training. Some well-known applications include data mining, which involves extracting new information from existing data, or using algorithms to classify images based on the things contained within them.

The algorithms used for machine learning get trained through one of three ways: supervised, unsupervised and semi-supervised learning. Here are some of the things people should know about each method.

Supervised Learning Involves Sets of Labeled Data

Supervised learning is the most common way to train a machine learning algorithm. A set of labeled data is used with this method. The algorithm attempts to understand the difference between the input (untrained) data and the tagged information, which is the output data. When it can do that with sufficient accuracy, it’s thoroughly trained.

There are two categories for supervised learning. With regression, the algorithm predicts a numerical value based on the information it already knows. For example, it could work well when guessing height and weight or making predictions about the stock market.

Alternatively, people can use the second category, which is classification. Then, the algorithm assesses the input data and determines how to group it with similar items. In that case, the input data might be a picture of a horse, and the algorithm would recognize it as an animal rather than a piece of furniture.

There’s No Labeled Data in Unsupervised Learning

When using unsupervised data in machine learning, people cannot train the algorithms in the same way described above. That’s because the output data is unknown, and therefore, unlabeled. If a company is evaluating supervised and unsupervised learning in data mining, that approach could work well in some instances, like determining the target market for a product that hasn’t launched yet.

Since the new algorithm can’t rely on a data set for training, it must detect patterns in the information independently. In most cases, supervised learning is more applicable to real-world problems due to the known output values. When people working with a machine learning algorithm don’t have the outputs, they don’t have an accurate way to assess its accuracy.

In contrast, people can look at a machine learning algorithm trained through supervised learning and compare the information with the training data set with the results.

With that in mind, it’s still useful for people to know about a type of unsupervised learning called clustering. It assigns data points into appropriate groups based on the level of similarity or dissimilarity between them.

One possible way to use this type of training is when applying machine learning to the cybersecurity field and preparing the algorithm to offer anomaly detection across a network. It could help spot previously unseen threats.

What About Semi-Supervised Learning?

The most straightforward way to define semi-supervised learning is to discuss it as something that falls between supervised and unsupervised learning. Generally, there is a large set of unlabeled data and a smaller set of labeled information. The labeled collection helps improve the overall results of the training since there are some known factors.

People sometimes prefer using the semi-supervised method of training algorithms for a couple of reasons. Labeling all the data before beginning to train the algorithm can be a time- and cost-intensive process.

Moreover, since humans have to choose the labels for the training data set, they could unintentionally introduce elements of bias to the algorithm as it’s exposed to those hidden preferences during training.

Website classification and speech recognition are two potential ways to use semi-supervised learning. In one recent example, researchers working with the Alexa assistant for Amazon smart speakers cut speech recognition errors by as much as 22% by using semi-supervised learning. The team used 1 million hours of unlabeled data and 7,000 hours of labeled information.

One of the advantages people might notice by using this method is that it might create a new category for data that the humans responsible for labeling didn’t think of earlier.

No Single Best Option to Choose

Whether people are curious about supervised and unsupervised learning in data mining or want to understand more about why taking the semi-supervised approach might be the best option, they should realize that none of these methods is the most appropriate one in every case.

Instead, it’s necessary for individuals to assess the specifics of their projects and take other factors into account, such as their budget and schedule. Then, it should be easier for them to pick which learning method to use.

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