DTSA 5504 DATA MINING PIPELINE STUDY
GUIDE ACTUAL EXAM 2026 QUESTIONS WITH
ANSWERS GRADED A+
◉ ______________ occurs when the analyst builds a model that does a
great job of explaining the sample of data on which it is based, but fails
to accurately predict outside the sample data. Answer: Model overfitting
◉ A tree that classifies a categorical outcome variable by splitting
observations into groups via a sequence of hierarchical rules is called
a(n). Answer: classification tree
◉ As we increase the cutoff value, _______ error will decrease and
_________ error will rise. Answer: Class 0, Class 1
◉ A _________ chart compares the number of actual Class 1
observations identified if considered in decreasing order of their
estimated probability of being in Class 1 and compares this to the
number of actual Class 1 observations identified if randomly selected.
Answer: cumulative lift
◉ __________ is the step in data mining that includes addressing
missing and erroneous data, reducing the number of variables, defining
new variables, and data exploration. Answer: Data preparation
, ◉ Instead of Y as outcome variable, in logistic regression, we use a
_______ called the logit. Answer: function of Y
◉ If the outcome variable is ________, we can use logistic regression as
a classification tool. Answer: binary
◉ _____ is a category of data-mining techniques in which an algorithm
learns how to predict or classify an outcome variable of interest.
Answer: Supervised learning
◉ An important part of ________ is applying the chosen model to the
test data as a final evaluation of model performance. Answer: model
assessment
◉ __________ is a method of extracting data relevant to the business
problem under consideration. It is the first step in the data mining
process. Answer: Data sampling
◉ ______ is one minus the overall error rate. Answer: Accuracy
◉ A characteristic or quantity of interest that can take on different values
is a(n). Answer: variable
GUIDE ACTUAL EXAM 2026 QUESTIONS WITH
ANSWERS GRADED A+
◉ ______________ occurs when the analyst builds a model that does a
great job of explaining the sample of data on which it is based, but fails
to accurately predict outside the sample data. Answer: Model overfitting
◉ A tree that classifies a categorical outcome variable by splitting
observations into groups via a sequence of hierarchical rules is called
a(n). Answer: classification tree
◉ As we increase the cutoff value, _______ error will decrease and
_________ error will rise. Answer: Class 0, Class 1
◉ A _________ chart compares the number of actual Class 1
observations identified if considered in decreasing order of their
estimated probability of being in Class 1 and compares this to the
number of actual Class 1 observations identified if randomly selected.
Answer: cumulative lift
◉ __________ is the step in data mining that includes addressing
missing and erroneous data, reducing the number of variables, defining
new variables, and data exploration. Answer: Data preparation
, ◉ Instead of Y as outcome variable, in logistic regression, we use a
_______ called the logit. Answer: function of Y
◉ If the outcome variable is ________, we can use logistic regression as
a classification tool. Answer: binary
◉ _____ is a category of data-mining techniques in which an algorithm
learns how to predict or classify an outcome variable of interest.
Answer: Supervised learning
◉ An important part of ________ is applying the chosen model to the
test data as a final evaluation of model performance. Answer: model
assessment
◉ __________ is a method of extracting data relevant to the business
problem under consideration. It is the first step in the data mining
process. Answer: Data sampling
◉ ______ is one minus the overall error rate. Answer: Accuracy
◉ A characteristic or quantity of interest that can take on different values
is a(n). Answer: variable
