GT ISYE 6501 Final Exam 2025 | All Questions
and Correct Answers | Graded A+ | Verified
Answers | Just Released
Suited for: Variable Selection and/or prediction from feature data. Def: Variable
selection process that can combine forward selection and backward regression ---
------CORRECT ANSWER-----------------Stepwise regression
Suited for Variable Selection and/or prediction from feature data. Def: Method
for limiting the number of variables in a model by limiting the sum of all
coefficients' absolute values. Can be helpful when number of data points is less
than number of factors. ---------CORRECT ANSWER-----------------Lasso Regression
Suited for: experimental design. Def Test of a subset of all possible combinations
of factor values over multiple factors. If chosen well, the desired effects of factors
and factor interaction effects can be obtained. ---------CORRECT ANSWER------------
-----Fractional Factorial Designs
Suited for Classification and/or prediction from feature data Def: Classification
algorithm that uses a boundary to separate the data into two ore more categories
("classes"). Used to Using feature data to predict whether or not something will
happen two time period in the future. ---------CORRECT ANSWER-----------------
Support Vector Machines
,Suited for: Clustering. Def: Clustering algorithm that defines k clusters of data
points, each corresponding to one of k clusters centers selected by the algorithm.
---------CORRECT ANSWER-----------------K-means algorithm
Suited for: Prediction from time-series data. Def: Data smoothing technique in
which older observations are assigned exponentially decreasing weights, so more
emphasis is given to recent observations. Analysis: Using time-series data to
predict the amount of something two time periods in the future. ---------CORRECT
ANSWER-----------------Exponential smoothing
Suited for: Prediction from time-series data. Definition: Autoregressive method
used to model variance in time series data. (Exam) Model used to: Using time-
series data to predict the variance of something two time period in the future. ----
-----CORRECT ANSWER-----------------GARCH
(Exam) Suited for: Prediction from time-series data. Definition: Time series model
that uses differences between observations when data is nonstationary. Also
called Box-Jenkins. (Exam) Model used to: Using time series to predict the amount
of something two time periods in the future ---------CORRECT ANSWER----------------
-ARIMA
(Exam): Suited for: Using feature data to predict the amount and/or probability of
something two time periods in the future. Definition: Regression model where a
data point's response is estimated based on the responses of the 𝑘𝑘 _nearest
,data points with known response. ---------CORRECT ANSWER-----------------K-
nearest neighbor regression
(Exam) Suited for: Using feature data to predict the probability of something
happening and/or weather or not something will happen two time period in the
future. Definition Logistic Regression model that uses an exponential function of
variables to estimate a response that is either between 0 and 1, or must be equal
to 0 or 1. Examples of Logistic Regression): Exam (Q43): Estimate the probability
that a patient survives heart transplant surgery. Another example: estimate the
likelihood that a flight from Atlanta to Detroit will take more than two hours. ------
---CORRECT ANSWER-----------------Logistic regression tree
Tree-based method for regression. After branching to split the data, each subset
is analyzed with its own regression model. ---------CORRECT ANSWER-----------------
Regression Tree
Iterative split (branching) of a data set into more-specific subsets that each are
modeled separately. Often used for classification, regression, and decision-
making. Also, can be used to solve optimization problems. ---------CORRECT
ANSWER-----------------Tree
(Exam) Model to: Using feature data to predict whether or not something will
happen two time periods in the future. ---------CORRECT ANSWER-----------------
Random Support Vector Machine Forest
, A set of multiple trees. Just like in real life. ---------CORRECT ANSWER-----------------
Forest
(Exam) Suited for: Using feature regression to predict the amount of something
two time periods in the future. ---------CORRECT ANSWER-----------------Linear
regression tree
Regression model where the relationships between attributes and a response are
modeled as linear functions. (Examples of Linear Regression): Exam (Q43)
Forecast the number of hotdogs that will be sold at a baseball game. Another
example: Estimate the amount of time it will take to process a certain loan ---------
CORRECT ANSWER-----------------Linear Regression
For each type of data specify if it is or it is not time series:
Definition Time Series: Data that records the same attribute/response at multiple
points in time (often at equal time intervals).
- Characteristics of a day (day of week, season, temperature, amount of rainfall)
that might affect the number of burgers sold: ---------CORRECT ANSWER--------------
---(EXAM) NOT TIME SERIES
For each type of data specify if it is or it is not time series:
Definition Time Series: Data that records the same attribute/response at multiple
points in time (often at equal time intervals).
and Correct Answers | Graded A+ | Verified
Answers | Just Released
Suited for: Variable Selection and/or prediction from feature data. Def: Variable
selection process that can combine forward selection and backward regression ---
------CORRECT ANSWER-----------------Stepwise regression
Suited for Variable Selection and/or prediction from feature data. Def: Method
for limiting the number of variables in a model by limiting the sum of all
coefficients' absolute values. Can be helpful when number of data points is less
than number of factors. ---------CORRECT ANSWER-----------------Lasso Regression
Suited for: experimental design. Def Test of a subset of all possible combinations
of factor values over multiple factors. If chosen well, the desired effects of factors
and factor interaction effects can be obtained. ---------CORRECT ANSWER------------
-----Fractional Factorial Designs
Suited for Classification and/or prediction from feature data Def: Classification
algorithm that uses a boundary to separate the data into two ore more categories
("classes"). Used to Using feature data to predict whether or not something will
happen two time period in the future. ---------CORRECT ANSWER-----------------
Support Vector Machines
,Suited for: Clustering. Def: Clustering algorithm that defines k clusters of data
points, each corresponding to one of k clusters centers selected by the algorithm.
---------CORRECT ANSWER-----------------K-means algorithm
Suited for: Prediction from time-series data. Def: Data smoothing technique in
which older observations are assigned exponentially decreasing weights, so more
emphasis is given to recent observations. Analysis: Using time-series data to
predict the amount of something two time periods in the future. ---------CORRECT
ANSWER-----------------Exponential smoothing
Suited for: Prediction from time-series data. Definition: Autoregressive method
used to model variance in time series data. (Exam) Model used to: Using time-
series data to predict the variance of something two time period in the future. ----
-----CORRECT ANSWER-----------------GARCH
(Exam) Suited for: Prediction from time-series data. Definition: Time series model
that uses differences between observations when data is nonstationary. Also
called Box-Jenkins. (Exam) Model used to: Using time series to predict the amount
of something two time periods in the future ---------CORRECT ANSWER----------------
-ARIMA
(Exam): Suited for: Using feature data to predict the amount and/or probability of
something two time periods in the future. Definition: Regression model where a
data point's response is estimated based on the responses of the 𝑘𝑘 _nearest
,data points with known response. ---------CORRECT ANSWER-----------------K-
nearest neighbor regression
(Exam) Suited for: Using feature data to predict the probability of something
happening and/or weather or not something will happen two time period in the
future. Definition Logistic Regression model that uses an exponential function of
variables to estimate a response that is either between 0 and 1, or must be equal
to 0 or 1. Examples of Logistic Regression): Exam (Q43): Estimate the probability
that a patient survives heart transplant surgery. Another example: estimate the
likelihood that a flight from Atlanta to Detroit will take more than two hours. ------
---CORRECT ANSWER-----------------Logistic regression tree
Tree-based method for regression. After branching to split the data, each subset
is analyzed with its own regression model. ---------CORRECT ANSWER-----------------
Regression Tree
Iterative split (branching) of a data set into more-specific subsets that each are
modeled separately. Often used for classification, regression, and decision-
making. Also, can be used to solve optimization problems. ---------CORRECT
ANSWER-----------------Tree
(Exam) Model to: Using feature data to predict whether or not something will
happen two time periods in the future. ---------CORRECT ANSWER-----------------
Random Support Vector Machine Forest
, A set of multiple trees. Just like in real life. ---------CORRECT ANSWER-----------------
Forest
(Exam) Suited for: Using feature regression to predict the amount of something
two time periods in the future. ---------CORRECT ANSWER-----------------Linear
regression tree
Regression model where the relationships between attributes and a response are
modeled as linear functions. (Examples of Linear Regression): Exam (Q43)
Forecast the number of hotdogs that will be sold at a baseball game. Another
example: Estimate the amount of time it will take to process a certain loan ---------
CORRECT ANSWER-----------------Linear Regression
For each type of data specify if it is or it is not time series:
Definition Time Series: Data that records the same attribute/response at multiple
points in time (often at equal time intervals).
- Characteristics of a day (day of week, season, temperature, amount of rainfall)
that might affect the number of burgers sold: ---------CORRECT ANSWER--------------
---(EXAM) NOT TIME SERIES
For each type of data specify if it is or it is not time series:
Definition Time Series: Data that records the same attribute/response at multiple
points in time (often at equal time intervals).
