Georgia Institute Of TechnologyISYE 6501 Midterm Quiz 1 Verified Summer 2021 SU21 Introduction to Analytics Modeling. Rated 100/100 Question 113/13 points (graded) Keyboard HelpDrag each of the 13 models/methods to one of the 5 categories of question it iscommonly used for, unless no correct category is listed for it. For models/methods thathave more than one correct category, choose any one
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Georgia Institute Of TechnologyISYE 6501 Midterm Quiz 1 Verified Summer 2021 SU21 Introduction to Analytics Modeling. Rated 100/100
Question 1
13/13 points (graded)
Keyboard Help
Drag each of the 13 models/methods to one of the 5 categories of question it is
commonly used for, unless no correct category is listed for it. For models/methods that
have more than one correct category, choose any one correct category; for
models/methods that have no correct category listed, do not drag them.
2.19/3.0 points (graded)
Select all of the following models that are designed for use with attribute/feature data (i.e., not timeseries data):
You have used 1 of 1 attempt
Information for Questions 3a, 3b, 3c
Figures A and B show the training data for a soft classification problem, using two
predictors (x and x ) to separate between black and white points. The dashed
lines are the classifiers found using SVM. Figure A uses a linear kernel, and Figure
B uses a nonlinear kernel that required fitting 16 parameter values.
FEEDBACK
Correctly placed 11 items.
Good work! You have completed this drag and drop problem.
Final attempt was used, highest score is 13.0
Submit You have used 1 of 1 attempts. Reset Show Answer
Exponential smoothing
ARIMA
k-means
Principal component analysis
Linear regression
k-nearest-neighbor
Random forest
CUSUM
Logistic regression
Support vector machine
GARCH
Information for Questions 3a, 3b, 3c
Figures A and B show the training data for a soft classification problem, using two
predictors (x and x ) to separate between black and white points. The dashed
lines are the classifiers found using SVM. Figure A uses a linear kernel, and Figure
B uses a nonlinear kernel that required fitting 16 parameter values.
Select all of the following statements that are true.
You have used 1 of 1 attempt
Question 3b
2.25/3.0 points (graded)
3b. Select all of the following statements that are true.
You have used 1 of 1 attempt
Question 3c
Question 3c
3.0/3.0 points (graded)
3c. Select all of the following statements that are true.
Figure A's classifier is based only on the value of .
Figure A's classifier is more likely to be over-fit than Figure B's classifier.
Figure A's classifier has a narrower margin than Figure B's classifier in the training data.
Figure A's classifier incorrectly classifies exactly 4 white points as black in the training data.
Figure A shows that the black point 7.2,1.4 is an outlier.
Submit
Figure B's classifier has a narrower margin in the training data than Figure A's classifier.
Figure B's classifier is more likely to be over-fit than Figure A's classifier.
Figure B's classifier incorrectly classifies exactly 5 white points in the training data.
Figure B shows that the black point 7.2,1.4 is an outlier.
Submit
A new point at 7, 1.5 would be classified as black by Figure A's classifier.
A new point at 7 1 5 would be classified as black by Figure B's classifier
8/2/2021 Midterm Quiz 1 | Midterm Quiz 1 -- Summer 2021 | SU21: Introduction to Analytics Modeling | edX
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You have used 1 of 1 attempt
Question 3d
3.0/3.0 points (graded)
In the soft classification SVM model where we select coefficients ... to minimize
3d. Select all of the following statements that are correct.
You have used 1 of 1 attempt
Question 3e
3.0/3.0 points (graded)
3e. In the hard classification SVM model, it might be desirable to put the classifier in a location that
has equal margin on both sides... (select all correct answers):
You have used 1 of 1 attempt
Information for Questions 4a, 4b, 4c
Seven different regression models have been fitted, using different sets of variables. The figure
below shows the resulting adjusted R-squared value for various models, as measured by crossvalidation.
A new point at 7, 1.5 would be classified as black by Figure B s classifier.
A new point at 7, 1.5 would be classified as black by a -nearest-neighbor algorithm for all
.
In Figure A, if the training data had 1000 more black points to the right of the classifier, a
1000-nearest-neighbor algorithm would classify a new point at 7,1.5 as black.
Question 4a
3.0/3.0 points (graded)
Which of the models would you expect to perform best on a test data set?
You have used 1 of 1 attempt
Question 4b
3.0/3.0 points (graded)
Under which of the following conditions would Model 2 be the most appropriate to use (select all
correct answers)?
Model 5, because it has the highest Adjusted .
Model 1, because it has much lower Adjusted .
Model 4, because its Adjusted only slightly lower than Model 5 and uses one fewer
predictor.
One of Models 2,3,4,5,6,7, but it's hard to be sure which because their Adjusted are so
close to each other.
Submit
Data collection for is too expensive for it to be used in the model.
Government regulations require using for this sort of model.
It is important to find the simplest good model.
The value of is not known in time for use in the model.
8/2/2021 Midterm Quiz 1 | Midterm Quiz 1 -- Summer 2021 | SU21: Introduction to Analytics Modeling | edX
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You have used 1 of 1 attempt
Additional Information for Question 4c
The table below shows the Akaike Information Criterion AIC, Corrected AIC, and Bayesian
Information Criterion BIC for each of the models.
Question 7
8/8 points (graded)
Keyboard Help
The figures below each show a data set that will be used in k-means clustering algorithms
(where distance between values is important).
Each data set has two attributes. For each data set, drag to it the data preparations that
are needed for k-means to work well on the data set.
FEEDBACK
Submit You have used 1 of 1 attempts. Reset Show Answer
First scaling and then
outlier removal
Only outlier removal
Neither outlier removal nor
scaling
Only scaling
First outlier removal and
then scaling
Information for Questions 8a, 8b
A regression tree approach was used to describe the effect of 7 different covariates (x1 through x7
on monthly sales. The tree is shown below. In each model, only the significant covariates are
shown.
Question 8a
5.0/5.0 points (graded)
Select all of the following statements that are true according to this regression tree:
You have used 1 of 1 attempt
Question 8b
2.0100000000000002/3.0 points (graded)
A random forest model was built for the same purpose, using the same 7 covariates. Which of the
following statements are true?
FEEDBACK
Correctly placed 4 items.
Good work! You have completed this drag and drop problem.
Final attempt was used, highest score is 8.0The effect of depends on the values of other variables.
could be replaced by a binary variable showing whether or not .
is irrelevant when predicting monthly sales.
The regression tree can predict monthly sales better for values of greater than or equal to
3.5 than for values of less than 3.5.
The effect of on sales is greatest when is small and is large ( and
).
Submit
The random forest model uses many trees, but returns a single tree solution that can be
analyzed.
8/2/2021 Midterm Quiz 1 | Midterm Quiz 1 -- Summer 2021 | SU21: Introduction to Analytics Modeling | edX
You have used 1 of 1 attempt
Information for Question 8c
A data scientist has run principal component analysis on the 7 covariates, with the following results:
Component | Eigenvalue
7 | 0.05Question 8c
4.0/4.0 points (graded)
Select all of the following statements that are correct:
You have used 1 of 1 attempt
You have reached the end of the quiz. Please make sure you have hit "submit" on every answer. If you have
remaining time and want to go back over any of the questions, do that before concluding the exam.
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