Answer / Charu Gupta

Dimensionality reduction in Machine Learning (ML) is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. This is important when dealing with high-dimensional data, as it can help improve model performance and reduce overfitting. Techniques for dimensionality reduction include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-Distributed Stochastic Neighbor Embedding (t-SNE).

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