Supervised vs. Unsupervised Learning In A Nutshell

Supervised vs. unsupervised learning describes two main types of tasks within the field of machine learning. In supervised learning, the researcher teaches the algorithm the conclusions or predictions it should make. In Unsupervised Learning, the model has algorithms able to discover and then present inferences about data. There is no teacher or single correct answer. Thus the machine learns on itself.

Understanding supervised vs. unsupervised learning

In machine learning, algorithms are trained according to the data available and the research question at hand. But if researchers fail to identify the objective of the machine learning algorithm, they will not be able to build an accurate model.

Ultimately, the ability to build an accurate model comes down to a matter of choice. Algorithms can be trained using one of two models that help them make predictions about data:

  • Supervised learning – where the researcher teaches the algorithm the conclusions or predictions it should make.
  • Unsupervised learning – where the algorithm is left to its own devices to discover and then present inferences about data. There is no teacher or single correct answer.

The next sections will look at each model in detail.

Supervised learning

In supervised learning, the researcher teaches the algorithm to use data that is well labeled. That is, some of the data is already tagged with the correct answer. Then, the algorithm is provided with a new set of examples called training data that it uses to produce a correct outcome based on the previously labeled data.

Supervised learning problems can be categorized as:

  • Classification problems – where the output variable is a category such as “green” and “yellow” or “yes” and “no”. Examples include spam detection, face detection analysis, and the automated marking of exams.
  • Regression problems – where the output variable is a real value, such as “dollars” or “kilograms”. Regression algorithms (linear regression models) are used in any scenario requiring a prediction of numerical values based on previous observations. Examples include house and stock price predictions and weather forecasting.

Unsupervised learning

Conversely, unsupervised learning involves training an algorithm with information that is neither labeled nor classified. Here, the algorithm must group unsorted information according to patterns or similarities in the data without prior training.

Unsupervised learning algorithms must deal with:

  • Clustering problems – where the goal is to discover inherent groupings in the data. For example, a marketing agency may use an algorithm to group customers by purchasing behavior. 
  • Association problems – where the algorithm must find association rules in the data. The same marketing agency may look at what consumers tend to buy or do after purchasing a certain product.
  • Anomaly problems – where the algorithm searches the data for rare items or events. Many financial institutions use anomaly algorithms to detect instances of fraud in bank account records. Antivirus software also uses similar technology to identify malware.

Choosing between supervised and unsupervised learning

Machine learning is a vast field and as a result, choosing the right machine learning process can be difficult and resource-intensive.

In very general terms though, assess these pointers:

  1. Evaluate data. Perhaps an obvious point, but one that is worth mentioning. Is it labeled or unlabeled? Could expert consultation facilitate additional labeling?
  2. Define the goal. Is the problem defined and likely to reoccur? Alternatively, will an algorithm have a better chance of identifying unknown problems ahead of time?
  3. Review the available algorithms. Which are best suited to the problem in terms of the number of features, attributes, or characteristics? Algorithm choice should also be sensitive to the overall structure and volume of data to be analyzed.
  4. Study historical applications. Where has the algorithm already been used to great success? Consider reaching out to organizations or individuals who have demonstrable skills in a comparable field.

Key takeaways:

  • Supervised vs. unsupervised learning describes the two model types used in machine learning.
  • In supervised learning, the researcher teaches the algorithm to arrive at a desirable answer given labeled data points. It has applications in examination marking, facial recognition, and weather forecasting.
  • In unsupervised learning, the algorithm must group unsorted information that is neither labeled nor classified without instruction. Unsupervised learning has important uses in detecting bank fraud and malware. It is also used to identify patterns in consumer buying behavior.

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Connected Concepts

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DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.
RevOps – short for Revenue Operations – is a framework that aims to maximize the revenue potential of an organization. RevOps seeks to align these departments by giving them access to the same data and tools. With shared information, each then understands their role in the sales funnel and can work collaboratively to increase revenue.
AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.
Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.
Ad Ops – also known as Digital Ad Operations – refers to systems and processes that support digital advertisements’ delivery and management. The concept describes any process that helps a marketing team manage, run, or optimize ad campaigns, making them an integrating part of the business operations.

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