What is the significance of minimizing within-class variance in LDA?

  • It decreases model accuracy
  • It enhances separation between different classes
  • It maximizes the similarity between classes
  • It reduces computational complexity
Minimizing "within-class variance" in LDA ensures that data points within the same class are close together. This enhances the separation between different classes, leading to improved discrimination and classification performance.
Add your answer
Loading...

Leave a comment

Your email address will not be published. Required fields are marked *