Data Science

caret

Question 1
Marks : +2 | -2
Pass Ratio : 100%
Which of the following function can create the indices for time series type of splitting?
newTimeSlices
createTimeSlices
binTimeSlices
none of the mentioned
Explanation:
Rolling forecasting origin techniques are associated with time series type of splitting.
Question 2
Marks : +2 | -2
Pass Ratio : 100%
caret does not use the proxy package.
True
False
Explanation:
caret uses the proxy package.
Question 3
Marks : +2 | -2
Pass Ratio : 100%
Which of the following model sums the importance over each boosting iteration?
Boosted trees
Bagged trees
Partial least squares
None of the mentioned
Explanation:
gbm package can be used here.
Question 4
Marks : +2 | -2
Pass Ratio : 100%
Which of the following package tools are present in caret?
pre-processing
feature selection
model tuning
all of the mentioned
Explanation:
There are many different modeling functions in R.
Question 5
Marks : +2 | -2
Pass Ratio : 100%
Point out the wrong statement.
Simple random sampling of time series is probably the best way to resample times series data.
Three parameters are used for time series splitting
Horizon parameter is the number of consecutive values in test set sample
All of the mentioned
Explanation:
Simple random sampling of time series is probably not the best way to resample times series data.
Question 6
Marks : +2 | -2
Pass Ratio : 100%
Which of the following can be used to impute data sets based only on information in the training set?
postProcess
preProcess
process
all of the mentioned
Explanation:
This can be done with K-nearest neighbors.
Question 7
Marks : +2 | -2
Pass Ratio : 100%
Which of the following function tracks the changes in model statistics?
varImp
varImpTrack
findTrack
none of the mentioned
Explanation:
GCV change value can also be tracked.
Question 8
Marks : +2 | -2
Pass Ratio : 100%
For most classification models, each predictor will have a separate variable importance for each class.
True
False
Explanation:
The exceptions are classification trees, bagged trees and boosted trees.
Question 9
Marks : +2 | -2
Pass Ratio : 100%
Which of the following function can be used to flag predictors for removal?
searchCorrelation
findCausation
findCorrelation
none of the mentioned
Explanation:
Some models thrive on correlated predictors.
Question 10
Marks : +2 | -2
Pass Ratio : 100%
Which of the following function can be used to create balanced splits of the data?
newDataPartition
createDataPartition
renameDataPartition
none of the mentioned
Explanation:
If the y argument to this function is a factor, the random sampling occurs within each class and should preserve the overall class distribution of the data.