Dimensionality Reduction

Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. It can be divided into feature selection and feature extraction. Feature selection selects a subset of the original features, while feature extraction transforms the data into a lower-dimensional space. The goal is to simplify the data without losing important information, making it easier to analyze and model.

Visit the following resources to learn more:

• @article@What is Dimensionality Reduction?
• @video@Machine Learning - Dimensionality Reduction