Supervised learning

Supervised learning is a machine learning technique that requires labeled training data.

In supervised learning, algorithms model the relationship between features and labels during the learning phase. The training algorithm uses a known dataset (called the training dataset) to make predictions.

The data (including observations, measurements, and so on) is labeled with predefined classes.

For example, let's say we are given a collection of balls of different sizes with category labels such as big and small attached to them; we should be able to categorize them using supervised learning by considering their attributes, such as radius and label values.

Target variables and predictor variables

There are two types of variables in classification and regression, as follows:

• Target variables: These are the variables that should be the output

• Predictor variables: These are the observations or the variables that are mapped to the target variable

The target variable represents the output...