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What is Dimensionality Reduction in Machine Learning about?

Dimensionality reduction is a technique used to reduce the number of features in a dataset while preserving important information, addressing issues like overfitting and the curse of dimensionality in machine learning. It includes methods such as feature selection and feature extraction, with techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) being commonly used. The process is crucial for improving model performance and visualization, but care must be taken as it can

Author

klalol2121

Language

EN