ISYE 6501 FINAL EXAM COMPLETE QUESTIONS AND DETAILED SOLUTIONS LATEST UPDATE THIS YEAR JUST RELEASED

ISYE 6501 FINAL EXAM
COMPLETE QUESTIONS AND DETAILED SOLUTIONS
LATEST UPDATE THIS YEAR JUST RELEASED



Question 1: Select the analytics task that the R function, predict, is
directly suitable for. - Cross-validation - Graphing - Holt-Winters -
k-means - k-nearest-neighbor - Linear regression - Make
predictions from models - PCA - Random forest - Scale data - Train
various models - Support vector machine - None of the other
choices
Answer:

Make predictions from models


Question 2: Select the analytics task that the R function,
HoltWinters, is directly suitable for. - Cross-validation - Graphing -
Holt-Winters - k-means - k-nearest-neighbor - Linear regression -
Make predictions from models - PCA - Random forest - Scale data -
Train various models - Support vector machine - None of the other
choices
Answer:

Holt-Winters


Question 3: Select the analytics task that the R function,
randomForest, is directly suitable for. - Cross-validation - Graphing
- Holt-Winters - k-means - k-nearest-neighbor - Linear regression -
Make predictions from models - PCA - Random forest - Scale data -
Train various models - Support vector machine - None of the other
choices
Answer:

, Random forest


Question 4: Select the analytics task that the R function, prcomp, is
directly suitable for. - Cross-validation - Graphing - Holt-Winters -
k-means - k-nearest-neighbor - Linear regression - Make
predictions from models - PCA - Random forest - Scale data - Train
various models - Support vector machine - None of the other
choices
Answer:

PCA


Question 5: Select the analytics task that the R function, kmeans, is
directly suitable for. - Cross-validation - Graphing - Holt-Winters -
k-means - k-nearest-neighbor - Linear regression - Make
predictions from models - PCA - Random forest - Scale data - Train
various models - Support vector machine - None of the other
choices
Answer:

k-means


Question 6: Select the analytics task that the R function, cv, is
directly suitable for. - Cross-validation - Graphing - Holt-Winters -
k-means - k-nearest-neighbor - Linear regression - Make
predictions from models - PCA - Random forest - Scale data - Train
various models - Support vector machine - None of the other
choices
Answer:

Cross-validation

, Question 7: Select the analytics task that the R function, lm, is
directly suitable for. - Cross-validation - Graphing - Holt-Winters -
k-means - k-nearest-neighbor - Linear regression - Make
predictions from models - PCA - Random forest - Scale data - Train
various models - Support vector machine - None of the other
choices
Answer:

Linear Regression


Question 8: Which of the following three statements is correct? -
Every model's expected performance on training data will be the
same as its expected performance on the validation data, because
both the training data and the validation data are taken from the
same population. - Every model's expected performance on
training data will be worse than its expected performance on the
validation data, because the training data and the validation data
are different. - Every model's expected performance on training
data will be better than its expected performance on the validation
data, because model fits partly to random patterns in the training
data.
Answer:

Every model's expected performance on training data will be better than its
expected performance on the validation data, because model fits partly to
random patterns in the training data.


Question 9: Which of the following three statements is correct? -
The selected model's expected performance on test data will be
better than its expected performance on the validation data,
because there is a selection bias: the selected model is more likely
to have worse-than-average performance on random patterns in the
validation data. - The selected model's expected performance on