Que 12 / 13 pts stion 1
1. For each of the 13 models/methods, select the
choice that includes the category of question it is
commonly used for. For
... [Show More] models/methods that have
more than one correct category, the one it is most
Answer 1:
Answer 2:
commonly used for; for models/methods that have no
correct category listed, select"None".
i. ARIMA Response prediction
ii. CART Classification and Response prediction
iii. Cross validation Validation
iv. CUSUM None of the other choices
v. Exponential smoothing Response prediction
vi. GARCH Variance estimation
vii. kmeans Clustering
viii. k-nearest-neighbor Classification and Response
prediction
ix. Linear regression Response prediction
x. Logistic regression Classification and Response
prediction
xi. Principal component analysis Classification and
Response prediction
xii. Random forest Classification and Response
prediction
xiii. Support vector machine Classification
CCoorrrreecctt!! Response prediction
CCoorrrreecctt!! Classification and Response prediction
Answer 3:
Answer 4:
Answer 5:
Answer 6:
Answer 7:
Answer 8:
Answer 9:
Answer 10:
Answer 11:
CCoorrrreecctt!! Validation
CCoorrrreecctt!! None of the other choices
CCoorrrreecctt!! Response prediction
CCoorrrreecctt!! Variance estimation
CCoorrrreecctt!! Clustering
CCoorrrreecctt!! Classification and Response prediction
CCoorrrreecctt!! Response prediction
CCoorrrreecctt!! Classification and Response prediction
Answer 12:
Answer 13:
YYoouu AAnnsswweerreedd Classification and Response prediction
CCoorrrreecctt AAnnsswweerr None of the other choices
CCoorrrreecctt!! Classification and Response prediction
CCoorrrreecctt!! Classification
Q 3 / 3 pts uestion 2
2. For each of the following models, specify whether it
is designed for use with attribute/feature data or
time-series data:
a. k-nearest-neighbor
b. Support vector machine
c. Random forest
d. GARCH Time series data
e. Logistic regression
f. Principal component analysis
g. Exponential smoothing
h. Linear regression
i. CUSUM
Answer 1:
Answer 2:
Answer 3:
Answer 4:
Answer 5:
Answer 6:
Answer 7:
Answer 8:
j. ARIMA
k. k-means
CCoorrrreecctt!! Attribute/feature data
CCoorrrreecctt!! Attribute/feature data
CCoorrrreecctt!! Attribute/feature data
CCoorrrreecctt!! Time series data
CCoorrrreecctt!! Attribute/feature data
CCoorrrreecctt!! Attribute/feature data
CCoorrrreecctt!! Time series data
Answer 9:
Answer 10:
Answer 11:
CCoorrrreecctt!! Attribute/feature data
CCoorrrreecctt!! Time series data
CCoorrrreecctt!! Time series data
CCoorrrreecctt!! Attribute/feature data
INFORMATION FOR QUESTIONS 3-12
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.
1
2
Figure A
Figure B
INSTRUCTIONS FOR QUESTIONS 3-11
For each statement in Questions 3-11, select the
choice that makes the statement true.
Que 0.6 / 0.6 pts stion 3
Answer 1:
Figure A's classifier IS based only on the value of x1.
CCoorrrreecctt!! IS
Q 0.6 / 0.6 pts uestion 4
Figure A has MORE classification errors in the
training data than Figure B.
Answer 1:
CCoorrrreecctt!! MORE
Q 0.6 / 0.6 pts uestion 5
Answer 1:
Figure A's classifier has a WIDER margin than Figure
B's classifier in the training data.
CCoorrrreecctt!! WIDER [Show Less]