Answer / Arpit Gautam

Dimension Reduction is the process of transforming a high-dimensional dataset into a lower-dimensional space while retaining most of the original information. This helps to reduce overfitting, improve computation efficiency, and visualize complex data. Techniques for dimensionality reduction include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Non-negative Matrix Factorization (NMF), and t-SNE.

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