Supervised learning: definition and applications

Machine learning technology is capable of processing large amounts of data, and makes "intelligent" machines learn new behaviours to achieve results. Depending on the needs of companies, different learning models are applied, although supervised learning is one of the most widely used methods. Why?

Find out what supervised learning or machine learning is.
Communication Team


Reading time: 4 min

Today, artificial intelligence (AI) is playing an increasingly important role in organisations. This technology replicates in machines human capabilities such as reasoning, problem-solving and learning. Thus, machines can perform tasks such as managing social networks or customer service functions, and it is increasingly gaining ground in sectors such as healthcare and finance.

But machines need to be trained to improve their performance, through different machine learning models, depending on the tasks to be performed and the type of data available, there are three different categories: supervised, unsupervised and reinforcement learning.

What is supervised learning?

Supervised learning comes from machine learning and AI, and is a machine learning technique that uses packets of data to train the algorithms used to categorise information and predict outcomes. Thus, when new data is introduced, the results provided by the machines are adjusted according to the objective to be achieved.

This machine learning model allows organisations to solve a wide variety of specific issues in real time, e.g. classifying files, understanding and detecting factors that can lead to fraud, quantifying the number of people in an image, creating recommendations on content platforms or filtering spam etc.

How does supervised learning work?

The data packets used in supervised machine learning are used to train the algorithms and generate information to help make decisions. In this case, algorithms use an input with labelled data for training and try to find a function that assigns other output labels based on the input variables, telling the algorithm the goal, what it is looking for.

The algorithm increases the accuracy of the “output” data by training on the history it handles, and learns to reduce the margin of error in its predictions.

There are two different learning models. The first, classification, is used for digit identification tasks or to generate prescriptions. For example, in predicting customer migration or detecting the most listened music genre or detecting spam mail.

The second, regression, is used to predict methods of action, such as forecasting a user’s use of a particular service, and there are three types: linear, which trains the algorithm to find a linear relationship between the input and output data; logistic, which indicates the probability of an event occurring; and polynomial, which is used for more complex data, as it eliminates bias.

Types of supervised learning algorithms

Several types of algorithms are used in supervised machine learning processes and these are some of the most commonly used:

  • Decision trees. From a set of data, this predictive model produces logically constructed diagrams, which are used to categorise a list of repetitive constraints. These predictions can be used to solve problems.
  • Naïve Bayes. The behaviour of these algorithms is based on “Bayes’ theorem”, and calculates the probability that event A will occur if event B has occurred. This technique is mainly used in recommender systems or in industry to detect manufacturing failures.
  • Logistic regression. This can predict the conclusions of a categorical variable based on the analysis of dependent and independent variables. It is mainly used in the fields related to social sciences and health sciences.
  • Set of classifiers. These algorithms design a set of classifiers and then catalogue the new data being collected and weight them according to the predictions.
  • Support Vector Machines (SVM). This algorithm is used to solve classification and regression problems by creating a hyperplane where the distance between two points is the maximum.

Examples of supervised machine learning

Supervised machine learning models have multiple organisational applications in all kinds of sectors. These algorithms are a great ally for image recognition, for example, as they find, enclose and categorise objects. They are also used for predictive analytics, as they provide comprehensive information about the most relevant points in companies. In this way, they help leaders to justify decisions or make changes for the benefit of the business.

On the other hand, companies can analyse customer sentiment. By using supervised machine learning algorithms, organisations can analyse large volumes of data, including action contexts, emotions and intent. In this way, they understand the ultimate customer behaviour.

In addition, these algorithms are a powerful spam detection tool. Companies can train databases to recognise patterns of behaviour or anomalies in new data received in the inbox.

Challenges of supervised learning algorithms

Supervised machine learning offers multiple benefits to organisations: above all, it provides greater self-regulation and extraction of more comprehensive data for informed decision making. However, it poses a number of challenges. Primarily, some training and experience is required to operate these algorithms.

Algorithms also take some time to train. On the other hand, they are not free from human error and bias, which can lead to incorrect learning.


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