Which of the following techniques is used for dimensionality reduction while preserving the data's variance?
Principal component analysis, or PCA, is a dimensionality reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
Read the given statements and conclusions carefully. Assuming that the information given in the statements is true, even if it appears to be at varianc...
Statements: All watches are wall clocks.
Some wall clocks are calculators.
Conclusions: I. No watch is a calculator.
II. Some cal...
Statement:
At-least some J are K.
Only L are K.
Only M are J.
Few M are N.
Conclusion :
1. Some M are L.
Statements:
No Planet is a Moon
Some Moon are Star
All Axis are Planet
Only a few Sun are Axis
Conclusi...
Statements:
All gold are silvers.
All silvers are diamonds.
Some diamonds are ruby
No ruby is silver.
Conclusio...
Statements : All tools are menus.
No status is a menu.
All titles are status.
Conclusions: All menus being tools is a possi...
Statement:
Some P are Q
Only a few R are S.
Only a few S are T.
Only S are P.
Conclusion: ...
Statements:
Some copy are not book.
Only a few book are paper.
Only paper are notebook.
Conclusions:
Some copy may b...
Two statements are given followed by three conclusions numbered I, II, and III assuming the statements to be true, even if they seem to be at variance ...
At least some singers are dancers. Some dancers are not lyricist.
