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

  1. Regression

  2. 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:

  1. Linear Regression

  2. Logistic Regression

  3. 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:

  1. Linear Classifiers

  2. Support Vector Machines

  3. Decision Trees

  4. K-Nearest Neighbor

  5. Random Forest

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