How does LDA specifically maximize between-class variance while minimizing within-class variance?
- By finding the eigenvectors of the scatter matrices
- By finding the vectors that maximize the ratio of between-class scatter to within-class scatter
- By setting thresholds for class labels
- By using gradient descent
LDA specifically maximizes between-class variance and minimizes within-class variance by "finding the vectors that maximize the ratio of between-class scatter to within-class scatter." This ensures optimal class separation.
Loading...
Related Quiz
- After applying PCA to your dataset, you find that some Eigenvectors have very small corresponding Eigenvalues. What does this indicate, and what action might you take?
- The _________ linkage method in Hierarchical Clustering minimizes the variance of the distances between clusters.
- Regularization techniques help in preventing overfitting. Which of these is NOT a regularization technique: Batch Normalization, Dropout, Adam Optimizer, L1 Regularization?
- Gaussian Mixture Models (GMMs) are an extension of k-means clustering, but instead of assigning each data point to a single cluster, GMMs allow data points to belong to multiple clusters based on what?
- What is the primary difference between the Gini Index and entropy when used in Decision Trees?