Data Science Multiple Choice Questions on “Predicting with Regression”.
1. Predicting with trees evaluate _____________ within each group of data.
a) equality
b) homogeneity
c) heterogeneity
d) all of the mentioned
Answer: b
Explanation: Predicting with trees is easy to interpret.
2. Point out the wrong statement.
a) Training and testing data must be processed in different way
b) Test transformation would mostly be imperfect
c) The first goal is statistical and second is data compression in PCA
d) All of the mentioned
Answer: a
Explanation: Training and testing data must be processed in same way.
3. 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: d
Explanation: Bagging can be done using bag function as well.
4. 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: a
Explanation: Random forest is top performing algorithm in prediction.
5. Point out the correct statement.
a) Prediction with regression is easy to implement
b) Prediction with regression is easy to interpret
c) Prediction with regression performs well when linear model is correct
d) All of the mentioned
Answer: d
Explanation: Prediction with regression gives poor performance in non linear settings.
6. Which of the following library is used for boosting generalized additive models?
a) gamBoost
b) gbm
c) ada
d) all of the mentioned
Answer: a
Explanation: Boosting can be used with any subset of classifier.
7. The principal components are equal to left singular values if you first scale the variables.
a) True
b) False
Answer: b
Explanation: The principal components are equal to left singular values if you first scale the variables.
8. Which of the following is statistical boosting based on additive logistic regression?
a) gamBoost
b) gbm
c) ada
d) mboost
Answer: a
Explanation: mboost is used for model based boosting.
9. 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: b
Explanation: R has multiple boosting libraries.
10. PCA is most useful for non linear type models.
a) True
b) False
Answer: b
Explanation: PCA is most useful for linear type models.