What is dimensionality reduction in machine learning?

What is dimensionality reduction in machine learning?

Dimensionality reduction is a technique used to reduce the number of features in a dataset while preserving its essential information.

Why is it important to reduce dimensions?

Reducing dimensions helps to simplify models, improve performance, and reduce overfitting by eliminating noise and irrelevant features.

What are some common techniques for dimensionality reduction?

Common techniques include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminant Analysis (LDA).

Can you explain how PCA works?

PCA works by identifying the directions (principal components) in which the data varies the most and projecting the data onto these directions.