Many people now have an idea of what artificial intelligence is. It is changing our everyday lives, our interactions and our work. But within AI, a distinction is still made between supervised learning and unsupervised learning. In this “Innovation Explained,” we look at supervised learning.
Essentially, supervised learning is a subcategory of machine learning that deals with how to train an algorithm to get the best possible result in data classification. Simply put, it’s about, for example, how an AI manages to distinguish cats from dogs, apples from pears, or red from green. The most important thing to know about supervised learning is that they take labeled data sets to train the AI models. In the process, you give the machine precise values and features, and it continuously adjusts its information until it gets the most accurate result possible. The algorithm measures its accuracy in this process using the loss function and adjusts it until the error is sufficiently minimized.
In supervised learning, there are several types of problems for which it can be applied. The classification and the regression.
In classification, the algorithm classifies the test data into certain categories. For example, it recognizes whether the test data are cats, dogs or other and tries to make as many conclusions as possible based on the data available to it. Very common classification algorithms are linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbor and random forest, which are described in more detail below.
In contrast to classification, regression is used to understand the relationship between dependent and independent variables. For example, how are certain environmental conditions related to a company’s sales performance. Whenever forecasts are involved, this type of supervised learning is used. Methods used here include linear regression, logistic regression and polynomial regression are popular regression algorithms.
Supervised learning requires, at least in the beginning, a lot of training time and human attention. However, with more and more data sets and information, AI also becomes more intelligent and learns to train and learn on its own.