Dimensionality Reduction vs Feature Engineering

 

Aspect	Dimensionality Reduction	Feature Engineering
Purpose	Reduce the number of features by transforming them into a lower-dimensional space while preserving essential information	Create new features or modify existing ones to enhance the model's ability to capture patterns
Objective	Simplify data, reduce noise, and prevent overfitting by decreasing the dimensionality	Improve model performance by providing more informative features
Techniques	Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-distributed Stochastic Neighbor Embedding (t-SNE)	Scaling, encoding, interaction terms, mathematical operations, domain-specific feature creation
Automation	Can be automated through algorithms like PCA, but requires parameter tuning	May require domain knowledge and human expertise to create meaningful features
Data Transformation	Transforms the original features into a new coordinate system	Modifies the existing features or generates entirely new ones
Reduction of Features	Reduces the number of features by combining or selecting them based on their importance	May not necessarily reduce the number of features; it can increase or transform them
Examples	Reducing the dimensionality of image data with PCA	Creating polynomial features in a regression model
Use Cases	Useful when dealing with high-dimensional data or when simplifying complex data	Helpful for enhancing the model's predictive power or when domain-specific knowledge can provide valuable insights