Select the correct objectives of principal component analysis

The correct answer is E. All 1, 2 and 3. Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability…

Feature Engineering and Dimensionality Reduction

Question

Options

ATo reduce the dimensionality of the data set
BTo identify new meaningful underlying variables
CTo discover the dimensionality of the data set
DOnly 1 and 2
EAll 1, 2 and 3

How the community answered

(42 responses)

A
12% (5)
B
2% (1)
D
5% (2)
E
81% (34)

Explanation

Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible: and each succeeding component accounts for as much of the remaining variability as Objectives of principal component analysis 1. To discover or to reduce the dimensionality of the data set. 2. To identify new meaningful underlying variables.

Topics

#PCA#dimensionality reduction#unsupervised learning#feature extraction

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