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Dimensionality Reduction: Revision history

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10 June 2025

  • curprev 06:2006:20, 10 June 2025 Thakshashila talk contribs 2,413 bytes +37 →SEO Keywords
  • curprev 06:1106:11, 10 June 2025 Thakshashila talk contribs 2,376 bytes +2,376 Created page with "= Dimensionality Reduction = '''Dimensionality Reduction''' is a technique in machine learning and data analysis used to reduce the number of input variables (features) while preserving as much relevant information as possible. == Why Use Dimensionality Reduction? == High-dimensional data can lead to problems such as: * '''Overfitting:''' Too many features can cause the model to learn noise. * '''Increased Computation:''' More features = more time and resources...."
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