ML 3: Supervised Learning with Examples | Regression VS Classification

Supervised learning can be broadly classified into two categories: regression and classification.

• Regression: This involves predicting a continuous output variable based on one or more input features. For example, predicting house prices based on various factors such as location, size, and age of the property.

• Classification: This focuses on predicting categorical outcomes. An example would be determining whether an email is spam or not based on its content and sender.

Supervised learning can be categorized into two main types: regression and classification.

Regression is concerned with predicting a continuous output variable using one or more input features. For instance, this could involve estimating house prices by taking into account factors such as location, size, and age of the property.

On the other hand, classification is focused on predicting categorical outcomes. A typical example of classification would be identifying whether an email is spam based on its content and the identity of the