MCQOPTIONS
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MCQOPTIONS
This section includes 13 Mcqs, each offering curated multiple-choice questions to sharpen your Data Science knowledge and support exam preparation. Choose a topic below to get started.
| 1. |
PCA is most useful for non linear type models. |
| A. | True |
| B. | False |
| Answer» C. | |
| 2. |
Which of the following is one of the largest boost subclass in boosting? |
| A. | variance boosting |
| B. | gradient boosting |
| C. | mean boosting |
| D. | all of the mentioned |
| Answer» C. mean boosting | |
| 3. |
Which of the following is statistical boosting based on additive logistic regression? |
| A. | gamBoost |
| B. | gbm |
| C. | ada |
| D. | mboost |
| Answer» B. gbm | |
| 4. |
Which of the following library is used for boosting generalized additive models? |
| A. | gamBoost |
| B. | gbm |
| C. | ada |
| D. | all of the mentioned |
| Answer» B. gbm | |
| 5. |
Which of the following method options is provided by train function for bagging? |
| A. | bagEarth |
| B. | treebag |
| C. | bagFDA |
| D. | all of the mentioned |
| Answer» E. | |
| 6. |
WHICH_OF_THE_FOLLOWING_IS_ONE_OF_THE_LARGEST_BOOST_SUBCLASS_IN_BOOSTING_??$ |
| A. | variance boosting |
| B. | gradient boosting |
| C. | mean boosting |
| D. | all of the Mentioned |
| Answer» C. mean boosting | |
| 7. |
PCA_is_most_useful_for_non_linear_type_models.$ |
| A. | True |
| B. | False |
| Answer» C. | |
| 8. |
Which of the following is statistical boosting based on additive logistic regression ? |
| A. | gamBoost |
| B. | gbm |
| C. | ada |
| D. | mboost |
| Answer» B. gbm | |
| 9. |
The principal components are equal to left singular values if you first scale the variables. |
| A. | True |
| B. | False |
| Answer» C. | |
| 10. |
Which of the following library is used for boosting generalized additive models ? |
| A. | gamBoost |
| B. | gbm |
| C. | ada |
| D. | all of the Mentioned |
| Answer» B. gbm | |
| 11. |
Which of the following is correct with respect to random forest? |
| A. | Random forest are difficult to interpret but often very accurate |
| B. | Random forest are easy to interpret but often very accurate |
| C. | Random forest are difficult to interpret but very less accurate |
| D. | None of the Mentioned |
| Answer» B. Random forest are easy to interpret but often very accurate | |
| 12. |
Which of the following method options is provided by train function for bagging ? |
| A. | bagEarth |
| B. | treebag |
| C. | bagFDA |
| D. | all of the Mentioned |
| Answer» E. | |
| 13. |
Predicting with trees evaluate _____________ within each group of data. |
| A. | equality |
| B. | homogeneity |
| C. | heterogeneity |
| D. | all of the Mentioned |
| Answer» C. heterogeneity | |