Beginner · Data
Dimensionality reduction
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. The process of reducing the number of features or variables in a dataset while retaining essential information.
Technical Definition
The process of reducing the number of features or variables in a dataset while retaining essential information.
How it works
Dimensionality reduction is a technique used to simplify data by decreasing the number of features. This can involve either selecting the most relevant features (feature selection) or creating new, fewer features from the original ones (feature extraction). It helps to reduce computational costs, prevent overfitting, and improve model performance.
Related Concepts
- Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
- Machine Learning — A field of AI where systems learn patterns from data instead of following hard-coded rules.
- Principal component analysis (PCA) — A dimensionality reduction technique that transforms correlated variables into uncorrelated principal components, prioritizing variance.