What is the ‘curse of dimensionality’ in machine learning?

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What is the ‘curse of dimensionality’ in machine learning?

Key points

  • As the number of input features increases, the amount of data needed to cover the feature space grows exponentially.
  • Sparse data leads to overfitting and decreased model performance.
  • Models struggle to generalize well in high-dimensional spaces.

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