Data Science

Model Based Prediction

Question 1
Marks : +2 | -2
Pass Ratio : 33%
Predictive analytics is same as forecasting.
True
False
Explanation:
Predictive analytics goes beyond forecasting.
Question 2
Marks : +2 | -2
Pass Ratio : 33%
Which of the following function can be used for forecasting?
predict
forecast
ets
all of the mentioned
Explanation:
Forecasting is the process of making predictions of the future based on past and present data and analysis of trends.
Question 3
Marks : +2 | -2
Pass Ratio : 33%
Which of the following method can be used to combine different classifiers?
Model stacking
Model combining
Model structuring
None of the mentioned
Explanation:
Model ensembling is also used for combining different classifiers.
Question 4
Marks : +2 | -2
Pass Ratio : 33%
Which of the following is correct about regularized regression?
Can help with bias trade-off
Cannot help with model selection
Cannot help with variance trade-off
All of the mentioned
Explanation:
Regularized regression does not perform as well as random forest.
Question 5
Marks : +2 | -2
Pass Ratio : 33%
Which of the following function provides unsupervised prediction?
cl_forecast
cl_nowcast
cl_precast
none of the mentioned
Explanation:
cl_predict function is clue package provides unsupervised prediction.
Question 6
Marks : +2 | -2
Pass Ratio : 33%
Which of the following is used to assist the quantitative trader in the development?
quantmod
quantile
quantity
mboost
Explanation:
Quandl package is similar to quantmod.
Question 7
Marks : +2 | -2
Pass Ratio : 33%
Point out the wrong statement.
Model based approach may be computationally convenient
Model based approach use Bayes theorem
Model based approach are reasonably inaccurate on real problems
All of the mentioned
Explanation:
Model based approach are reasonably accurate on real problems.
Question 8
Marks : +2 | -2
Pass Ratio : 33%
Which of the following methods are present in caret for regularized regression?
ridge
lasso
relaxo
all of the mentioned
Explanation:
In caret one can tune over the no of predictors to retain instead of defined values for penalty.
Question 9
Marks : +2 | -2
Pass Ratio : 33%
Model based prediction considers relatively easy version for covariance matrix.
True
False
Explanation:
Model based prediction considers relatively easy version for covariance matrix.
Question 10
Marks : +2 | -2
Pass Ratio : 33%
Point out the correct statement.
Combining classifiers improves interpretability
Combining classifiers reduces accuracy
Combining classifiers improves accuracy
All of the mentioned
Explanation:
You can combine classifier by averaging.