Which technique is often used to handle scalability in machine learning models?

  • Dimensionality Reduction
  • Ensemble Learning
  • Feature Engineering
  • Reinforcement Learning
Dimensionality Reduction techniques, like Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE), are often used to handle scalability. They reduce the number of features while retaining essential information, making large datasets more manageable for machine learning models.
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