Question

Q9. Principle component analysis is able to reduce the

dimensionality of data but does not preserve data variation, thus
must be highly configured to a specific dataset.
1) True
2) False

Answer 1

PCA aims to reduce data dimensionality while preserving maximum variance and does not require high configuration specificity, making the statement false.

Step-by-step explanation:

The statement that principal component analysis (PCA) does not preserve data variation is false. In fact, one of the main goals of PCA is to reduce the dimensionality of data while preserving as much variance as possible. PCA works by identifying the axes that maximize the variance of the data. It might need to be tailored to fit a specific dataset in terms of deciding how many principal components to retain, but it does not inherently require highly specific configurations to a dataset as each principal component reflects a majority of the variation.