The method based on principal component analysis (PCA) evaluates the features according to

The correct answer is A. The projection of the largest eigenvector of the correlation matrix on the initial dimensions. Feature Selection: The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminate analysis evaluates. Them…

Design data storage solutions

Question

Options

AThe projection of the largest eigenvector of the correlation matrix on the initial dimensions
BAccording to the magnitude of the components of the discriminate vector
CThe projection of the smallest eigenvector of the correlation matrix on the initial dimensions
DNone of the above

How the community answered

(58 responses)

A
74% (43)
B
16% (9)
C
7% (4)
D
3% (2)

Explanation

Feature Selection: The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminate analysis evaluates. Them according to the magnitude of the components of the discriminate vector.

Topics

#PCA#eigenvectors#feature evaluation#dimensionality reduction

Community Discussion

No community discussion yet for this question.

Full DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Practice