ISYE 6501 FINAL SAMPLES QUIZ PREP EXAM WITH CORRECT AND VERIFIED ANSWERS

ISYE 6501 FINAL SAMPLES QUIZ PREP
EXAM WITH CORRECT AND VERIFIED
ANSWERS

For each of the four situations below, specify whether using a
variable selection approach like lasso or stepwise regression would
be important.

i. Time-series data is being used ______ don't use variable selection
ii. There are fewer data points than variables ______, use variable
selection
iii. There are too few data points to avoid overfitting if all variables
are included ______, use variable selection
iv. It is too costly to create a model with a large number of variables
______, use variable selection
No, don't use variable selection
Yes, don't use variable selection
Yes, don't use variable selection
Yes, don't use variable selection
Select the type of problem that Linear Regression is best suited for.

- Classification
- Clustering
- Experimental design
- Prediction from feature data
- Prediction from time-series data
- Variable selection
- Prediction from feature data

,Useful when you want to model the relationship between a
dependent variable and one or more independent variables with a
linear assumption.
Select the type of problem that ARIMA is best suited for.

- Classification and/or prediction from feature data
- Clustering
- Experimental design
- Prediction from time-series data
- Variable selection
- Prediction from Time-series data

Useful when you want to forecast future values in a time series (e.g.,
stock prices, sales) and have historical time-ordered data available.
Select the type of problem that logistic regression is best suited for.

- Classification and/or prediction from feature data
- Clustering
- Experimental design
- Prediction from time-series data
- Variable selection
- Classification and/or prediction from feature data

Useful when you want to model the probability of a binary outcome
(0 or 1) based on one or more predictor variables.
Select the type of problem that lasso regression is best suited for.

- Classification and/or prediction from feature data
- Clustering
- Experimental design

,- Prediction from time-series data
- Variable selection and/or prediction from feature data
- Variable Selection and/or prediction from feature data

Useful when you want to perform variable selection and
regularization in linear regression models, reducing the impact of
irrelevant features.
Select the type of problem that support vector machine is best
suited for.

- Classification and/or prediction from feature data
- Clustering
- Experimental design
- Prediction from time-series data
- Variable selection
- Classification and/or prediction from feature data

Useful when you want to classify data into different categories and
have labeled training data.
Select the type of problem that k-means is best suited for.

- Classification and/or prediction from feature data
- Clustering
- Experimental design
- Prediction from time-series data
- Prediction from feature data
- Variable selection
- Clustering

, Useful when you want to cluster data into k distinct groups based
on similarity and have unlabeled data.
Select the type of problem that GARCH is best suited for.

- Classification and/or prediction from feature data
- Clustering
- Experimental design
- Prediction from time-series data
- Prediction from feature data
- Variable selection
- Prediction from Time-series data

Useful when you want to model and forecast the volatility of
financial time series data (e.g., stock returns) and have data with
time-varying variance.
Select the type of problem that exponential smoothing is best suited
for.

- Classification and/or prediction from feature data
- Clustering
- Experimental design
- Prediction from time-series data
- Prediction from feature data
- Variable selection
- Prediction from time-series data

Useful when you want to generate short-term forecasts based on
weighted averages of past observations and have time series data
with trends or seasonality.