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.
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