# Supervised Learning

Types of ML Algorithm

This type of learning algorithm learns to map a function from a set of features (X) to a label/output (Y)

Supervised Learning is done on **labeled data**.

Supervised Learning algorithms can be further divided into two major parts

Regression

Classification

### Regression

In **Regression**, the output (Y) is usually *continuous (real number).*

Example: Predicting the amount of revenue generated by a store using features like the day of the week, month, season, footfall, etc.

Here, revenue will be the output/label (Y). Revenue is usually calculated as a real number, like $254, therefore it is continuous.

#### Algorithms:

Linear Regression

Logistic Regression

Polynomial Regression

### Classification

In **Classification**, the output (Y) is usually *discrete*.

Example: Separating images of dogs from the images of other animals.

Here, the output will be a discrete value, i.e. dog or not a dog (nothing in between).

#### Algorithms:

Linear Classifiers

Support Vector Machines

Decision Trees

K-Nearest Neighbor

Random Forest

Last updated