Dimension reduction techniques

Dimension reduction techniques are methods used to simplify complex data by reducing the number of variables or features while retaining the most important information. Think of it like summarizing a detailed report into key points so it’s easier to analyze and interpret. These techniques help visualize data, improve the efficiency of algorithms, and uncover patterns by focusing on the most meaningful aspects, making large, complicated datasets more manageable and insightful. Common methods include Principal Component Analysis (PCA) and t-SNE, which identify the most significant features to represent the data effectively.