Dimensionality Reduction is a technique used in unsupervised learning to reduce the number of features (dimensions) in a dataset while retaining the most important information. It helps simplify complex data, remove irrelevant or redundant features, and make the data easier to process and visualize, improving the efficiency of machine learning models.
The source passages do not explicitly define Dimensionality Reduction — it is a standard unsupervised learning concept from the broader CBSE AI syllabus not covered in the provided extracts. Examiners expect students to mention: (1) it reduces the number of features/variables, (2) it retains important information, and (3) it is used to simplify data and improve model performance. Avoid writing lengthy paragraphs — two to three crisp sentences are sufficient for 2 marks.