Dimensionality Reduction

Overview

Dimensionality reduction techniques reduce the number of features while preserving important information, improving model efficiency and enabling visualization of high-dimensional data.

When to Use

• High-dimensional datasets with many features
• Visualizing complex datasets in 2D or 3D
• Reducing computational complexity and training time
• Removing redundant or highly correlated features
• Preventing overfitting in machine learning models
• Preprocessing data before clustering or classification

Techniques

• PCA: Principal Component Analysis
• t-SNE: t-Distributed Stochastic Neighbor Embedding