Dimensionality Reduction

Dimensionality reduction is a crucial technique in data science and machine learning that involves reducing the number of features or dimensions in a dataset while retaining as much relevant information as possible. This process helps to simplify models, reduce computational costs, and mitigate the curse of dimensionality.

Common use cases for dimensionality reduction include:

• Data Visualization: Reducing high-dimensional data to 2D or 3D for easier visualization and interpretation.
• Noise Reduction: Eliminating irrelevant or redundant features that may introduce noise into the model.
• Feature Extraction: Creating new features that capture the essential information from the original high-dimensional data.
• Preprocessing for Machine Learning: Improving model performance by reducing overfitting and enhancing generalization.

Main algorithms used for dimensionality reduction tasks include:

• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Linear Discriminant Analysis (LDA)