Supervised learning

Supervised learning is a form of machine learning in which our data comes with a set of labels or a target variable that is numeric. These labels/categories usually belong to one feature/attribute, which is commonly known as the target variable. For instance, each row of your data could either belong to the category of Healthy or Not Healthy.

Given a set of features such as weight, blood sugar levels, and age, we can use the supervised machine learning algorithm to predict whether the person is healthy or not. 

In the following simple mathematical expression, S is the supervised learning algorithm, X is the set of input features, such as weight and age, and Y is the target variable with the labels Healthy or Not Healthy:

Although supervised machine learning is the most common type of machine learning that is implemented with scikit-learn and in the industry, most datasets typically do not come with predefined labels. Unsupervised learning algorithms are first used to cluster data without labels into distinct groups to which we can then assign labels. This is discussed in detail in the following section.