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What is Dimensionality Reduction Techniques Explained about?

Dimensionality reduction is a technique in machine learning that transforms high-dimensional data into a lower-dimensional space while preserving important information, addressing issues like overfitting and computational complexity. Techniques are categorized into linear methods, such as PCA and LDA, and nonlinear methods, such as t-SNE and UMAP, with applications in feature selection and extraction. Methods like Low Variance Filter and High Correlation Filter help improve model performance by selecting re

Author

himanshubhatt971

Language

EN