PCA is most useful for non linear type models. – McqOptions

MCQOPTIONS

Saved Bookmarks

→ Data Science
→ Binary Count Outcomes in Data Science
→ PCA is most useful for non linear type models...

1.	PCA is most useful for non linear type models.
A.	True
B.	False
Answer» C.

Discussion

No Comment Found

Related MCQs

PCA is most useful for non linear type models.
Which of the following is one of the largest boost subclass in boosting?
Which of the following is statistical boosting based on additive logistic regression?
Which of the following library is used for boosting generalized additive models?
Which of the following method options is provided by train function for bagging?
WHICH_OF_THE_FOLLOWING_IS_ONE_OF_THE_LARGEST_BOOST_SUBCLASS_IN_BOOSTING_??$
PCA_is_most_useful_for_non_linear_type_models.$
Which of the following is statistical boosting based on additive logistic regression ?
The principal components are equal to left singular values if you first scale the variables.
Which of the following library is used for boosting generalized additive models ?

Related Topics

Binary Count Outcomes in Data Science

Introduction Reproducible Research in Data Science

Regular Expressions Text Variables in Data Science

Cross Validation in Data Science

Summarizing Merging Data in Data Science

Caret in Data Science

Cli Git Workflow in Data Science

Probability Statistics in Data Science

Introduction Statistical Inference in Data Science

Exntrance Exams in Data Science

Recent Visits

Reply to Comment

Name *

Email *

Comment *

© Copyright 2025 McqOptions. All rights reserved.

Terms & Conditions
Privacy Policy
Disclaimer

Support: admin@mcqoptions.com