{"id":3179,"date":"2025-10-06T16:08:01","date_gmt":"2025-10-06T14:08:01","guid":{"rendered":"https:\/\/tecnologia.euroinnova.com\/que-es-el-aprendizaje-supervisado-y-como-funciona\/"},"modified":"2025-10-07T14:54:42","modified_gmt":"2025-10-07T12:54:42","slug":"supervised-learning","status":"publish","type":"post","link":"https:\/\/tecnologia.euroinnova.com\/en\/aprendizaje-supervisado","title":{"rendered":"What is supervised learning and how does it work?"},"content":{"rendered":"

Supervised learning is an approach that is implemented in the creation of artificial intelligence in which an algorithm is trained from previously classified data.<\/strong> The aim is to get them to associate a set of implicit patterns with a particular output. In this way, the algorithm will be able to provide satisfactory outputs when presented with completely different data.<\/span><\/p>\n

In this sense, the aim of supervised learning is for an artificial intelligence system to be able to perform regression and classification tasks with a high degree of accuracy.<\/strong> For example, it can be used to determine which category a blog post belongs to or to predict a sales index for the future. Supervised learning can also be applied in other AI-related processes, such as anomaly detection, the image recognition<\/a> or spam filtering.<\/span><\/p>\n

Supervised vs. unsupervised vs. semi-supervised learning<\/span><\/h2>\n

As we have explained in supervised learning, the model receives classified data, that is, each input comes with a label indicating the correct answer.<\/strong> The model learns to make predictions from the inputs and adjusts itself to minimise the difference between the predictions and the actual labels.<\/span><\/p>\n

In unsupervised learning, the model receives unlabelled data and looks for underlying patterns or structures.<\/strong> in the data without external help. The main objective of this machine learning technique is to establish associations between data or natural groupings in the data without a predetermined classification.<\/span><\/p>\n

Semi-supervised learning combines elements of supervised and unsupervised learning. Here, the model receives both labelled and unlabelled data.<\/strong> It uses labelled data to learn in a supervised way, but also uses unlabelled data to improve its ability to generalise and find patterns in the data.\u00a0<\/span><\/p>\n

When to apply supervised learning?<\/span><\/h2>\n

It is appropriate to use supervised learning in artificial intelligence in situations where we have labelled data, we know the output we want to obtain or if our goal is to formulate very accurate predictions from a labelled data set.\u00a0<\/span><\/p>\n

We can mention a couple of cases where it is better to apply supervised learning rather than unsupervised learning:<\/span><\/p>\n