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What is Dimensionality Reduction in Machine Learning about?

Dimensionality reduction is a crucial process in machine learning that simplifies datasets by reducing the number of features, thereby improving model accuracy and computational efficiency. Methods include feature selection and extraction, with Principal Component Analysis (PCA) being a prominent unsupervised learning algorithm that transforms correlated features into uncorrelated principal components. PCA is widely applied in various fields such as computer vision and finance for data visualization and pat

Author

bhardwaj.arttii

Language

EN