Dimensionality Reduction

Real-world datasets often come with dozens or even hundreds of features. Dimensionality reduction is the process of reducing the number of features in a dataset while retaining as much meaningful information as possible.

The Curse of Dimensionality

As the number of features grows, data becomes increasingly sparse in that high-dimensional space - distances between points start to lose meaning, models become harder to train, and the risk of overfitting rises sharply. This phenomenon is known as the curse of dimensionality.

Why Reduce Dimensions?

• Removes redundant or correlated features
• Speeds up training and reduces memory usage
• Helps visualize high-dimensional data in 2D or 3D
• Reduces noise and can improve model accuracy

Two Main Approaches

Feature Selection picks a subset of the original features and discards the rest, while Feature Extraction transforms the original features into a smaller set of new, combined features - PCA and SVD, which you'll learn next, are the most widely used feature extraction techniques.

Coming Up Next

The most widely used dimensionality reduction technique in Data Science is Principal Component Analysis (PCA), which you'll explore next.