Question

Which of the following statements is true about PCA?
(i) We must standardize the data before applying PCA.
(ii) We should select the principal components which explain the highest variance
(iii) We should select the principal components which explain the lowest variance
(iv) We can use PCA for visualizing the data in lower dimensions

A. (i), (ii) and (iv)
B. (ii) and (iv)
C. (iii) and (iv)
D. (i) and (iii)

Answer 1

hey friend u r answer is

A option is correct

Mark me as a brainlist

i think my answer is not right answer

u can report

Answer 2

Answer:

The correct answer to this question is Option A- (i), (ii), and (iv).

Explanation:

Before using PCA, we must standardize the data. The major components that best account for the variance should be chosen. To visualize the data in smaller dimensions, we can utilize PCA.

Why would someone utilize PCA?

PCA aids in data interpretation, although it doesn't always identify the key patterns. High-dimensional data can be made simpler through the use of principal component analysis (PCA), while still preserving trends and patterns. It accomplishes this by condensing the data into low dimensional that serve as feature summaries. In fields like facial recognition, computer vision, and image compression, PCA is primarily employed as a dimensionality reduction technique.

Thus, PCA removes correlated variables that are irrelevant to decision-making, it improved the quality of the Machine Learning algorithm.