Data Science

Predicting with Regression

Question 1
Marks : +2 | -2
Pass Ratio : 100%
Point out the correct statement.
Prediction with regression is easy to implement
Prediction with regression is easy to interpret
Prediction with regression performs well when linear model is correct
All of the mentioned
Explanation:
Prediction with regression gives poor performance in non linear settings.
Question 2
Marks : +2 | -2
Pass Ratio : 100%
Point out the wrong statement.
Training and testing data must be processed in different way
Test transformation would mostly be imperfect
The first goal is statistical and second is data compression in PCA
All of the mentioned
Explanation:
Training and testing data must be processed in same way.
Question 3
Marks : +2 | -2
Pass Ratio : 100%
Which of the following is one of the largest boost subclass in boosting?
variance boosting
gradient boosting
mean boosting
all of the mentioned
Explanation:
R has multiple boosting libraries.
Question 4
Marks : +2 | -2
Pass Ratio : 100%
PCA is most useful for non linear type models.
True
False
Explanation:
PCA is most useful for linear type models.
Question 5
Marks : +2 | -2
Pass Ratio : 100%
Which of the following is correct with respect to random forest?
Random forest are difficult to interpret but often very accurate
Random forest are easy to interpret but often very accurate
Random forest are difficult to interpret but very less accurate
None of the mentioned
Explanation:
Random forest is top performing algorithm in prediction.
Question 6
Marks : +2 | -2
Pass Ratio : 100%
Which of the following is statistical boosting based on additive logistic regression?
gamBoost
gbm
ada
mboost
Explanation:
mboost is used for model based boosting.
Question 7
Marks : +2 | -2
Pass Ratio : 100%
Predicting with trees evaluate _____________ within each group of data.
equality
homogeneity
heterogeneity
all of the mentioned
Explanation:
Predicting with trees is easy to interpret.
Question 8
Marks : +2 | -2
Pass Ratio : 100%
Which of the following library is used for boosting generalized additive models?
gamBoost
gbm
ada
all of the mentioned
Explanation:
Boosting can be used with any subset of classifier.
Question 9
Marks : +2 | -2
Pass Ratio : 100%
Which of the following method options is provided by train function for bagging?
bagEarth
treebag
bagFDA
all of the mentioned
Explanation:
Bagging can be done using bag function as well.
Question 10
Marks : +2 | -2
Pass Ratio : 100%
The principal components are equal to left singular values if you first scale the variables.
True
False
Explanation:
The principal components are equal to left singular values if you first scale the variables.