ISYE 6501 Final Exam Exam 2026/2027 | Questions and Correct Answers |
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Heuristic -answer-Algorithm that is not guaranteed to find the absolute best (optimal)
solution.
Hit rate -answer-Fraction of data points in a certain category that are correctly classified
by a model; equal to 𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹; also called the true positive rate, sensitivity,
and recall.
Holt-Winters method/Winters' method -answer-Three-parameter exponential smoothing
technique that incorporates trend and seasonality; also called triple exponential
smoothing.
Hypothesis test -answer-Statistical test to determine the probability that a property of a
sample of data is true for the whole population.
iid -answer-Independent and identically distributed.
Improving direction -answer-Vector of changes to a solution to an optimization problem,
such that the objective function gets better when moving the solution some distance in
the vector's direction.
Imputation -answer-Inserting values where data is missing
Independent -answer-A is "independent" of B if the probability or probability distribution
of A is not affected by B. For example, whether a coin flip is heads or tails is (I assume)
independent of the number of fish in the ocean exactly 100 years ago to this day, but
the temperature today is not independent of the temperature yesterday (if it was hot
yesterday, it's more likely to be hot today too, etc.).
,Independent and identically distributed (iid) -answer-Things that follow the same
probability distribution, including the same parameter(s), and whose values are
independent of each other. For example, multiple flips of the same coin are iid.
Infinity-norm -answer-Specific case of p-norm when 𝑝𝑝 = ∞. Sounds weird, but it just
reduces to the largest of the dimensions. If 𝑧𝑧 = (𝑧𝑧1, 𝑧𝑧2, ... , 𝑧𝑧𝑚𝑚) is a vector in an
𝑚𝑚-dimensional space, then its ∞-norm is max𝑖𝑖|𝑧𝑧𝑖𝑖|. If 𝑥𝑥 = (𝑥𝑥1, 𝑥𝑥2, ... , 𝑥𝑥𝑚𝑚) and
𝑦𝑦 = (𝑦𝑦1, 𝑦𝑦2, ... , 𝑦𝑦𝑚𝑚) are two points in an 𝑚𝑚- dimensional space, then the ∞-
norm distance between them is max𝑖𝑖|𝑥𝑥𝑖𝑖 − 𝑦𝑦𝑖𝑖|.
Initialization -answer-Setting starting values in an algorithm, or setting the first solution
value for an "direction/step-size" optimization algorithm.
Integer program -answer-Optimization model where the objective function is a linear
function of the variables, the constraints are linear equations and/or linear inequalities in
terms of the variables, and some or all variables are restricted to have integer values.
Interaction term -answer-Variable in a model that is the combination of two or more
other variables; for example, if 𝑥𝑥1 and 𝑥𝑥2 are variables, (𝑥𝑥1𝑥𝑥2) is an interaction
term/interaction variable.
Interarrival time -answer-The time between two consecutive arrivals of people, things,
etc. -- for example, the time between consecutive phone calls to a service hotline.
Iterate -answer-Repeat the same steps of a process.
k-fold cross-validation -answer-Validation technique where data is divided into several
parts ("folds"), and each part is used to validate a model fit to the remaining parts. Often
a more robust validation approach than splitting data into training and validation sets.
𝑘-means algorithm -answer-Clustering algorithm that defines 𝑘𝑘 clusters of data points,
each corresponding to one of 𝑘𝑘 cluster centers selected by the algorithm.
𝑘𝑘-Nearest-Neighbor (KNN) -answer-Classification algorithm that defines a data point's
category as a function of the nearest 𝑘𝑘 data points to it.
𝑘𝑘-Nearest-Neighbor regression -answer-Regression model where a data point's
response is estimated based on the responses of the 𝑘𝑘 nearest data points with known
response.
Kendall notation -answer-Notation to describe various types of queuing models -- for
example, M/M/c (a queue with Poisson arrivals, exponentially-distributed service times,
and c identical servers)
, Kernel -answer-A type of function that computes the similarity between two inputs;
thanks to what's (really!) sometimes known as the "kernel trick", nonlinear classifiers
can be found almost as easily as linear ones.
Knot -answer-Point where pieces of a spline regression meet.
Lasso/Lasso regression -answer-Method for limiting the number of variables in a model
by limiting the sum of all coefficients' absolute values. Can be very helpful when number
of data points is less than number of factors.
Leaf -answer-n a tree model, a subset of data from which there is no branching.
Learning -answer-Finding/discovering patterns (or rules) in data, often that can be
applied to new data.
LIFO -answer-Last-in, first-out: The last entity to join a queue is the first one to come
out -- for example, a stack of papers.
Likelihood -answer-Probability that a model with specific parameter values would
generate the actual outcomes in the data.
Linear combination -answer-The weighted sum of things. For example, if 𝑥𝑥1, 𝑥𝑥2, ... ,
𝑥𝑥𝑚𝑚 are factors, then 𝑎𝑎1𝑥𝑥1 + 𝑎𝑎2𝑥𝑥2 + ⋯ + 𝑎𝑎𝑚𝑚𝑥𝑥𝑚𝑚 is a weighted sum of them
for any numbers 𝑎𝑎1, 𝑎𝑎2, ... , 𝑎𝑎𝑚𝑚.
Linear equation -answer-Equation where a linear function is set equal to a constant or
another linear function.
Linear function -answer-Weighted sum of variables, plus a constant: 𝑎𝑎0 + ∑ 𝑎𝑎𝑖𝑖𝑥𝑥𝑖𝑖
𝑚𝑚 𝑖𝑖=1 .
Linear inequality -answer-Equation where a linear function is set equal to a constant or
another linear function.
Linear function -answer-Weighted sum of variables, plus a constant: 𝑎𝑎0 + ∑ 𝑎𝑎𝑖𝑖𝑥𝑥𝑖𝑖
𝑚𝑚 𝑖𝑖=1 .
Linear inequality -answer-Inequality where a linear function is set to be greater-than-or-
equal-to or less-than-or-equal-to a constant or another linear function.
Linear program -answer-An mathematical programming model where the objective
function is a linear function of the variables, and the constraints are linear equations
and/or linear inequalities in terms of the variables.
Already Graded A+ | 100% Verified
Heuristic -answer-Algorithm that is not guaranteed to find the absolute best (optimal)
solution.
Hit rate -answer-Fraction of data points in a certain category that are correctly classified
by a model; equal to 𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹; also called the true positive rate, sensitivity,
and recall.
Holt-Winters method/Winters' method -answer-Three-parameter exponential smoothing
technique that incorporates trend and seasonality; also called triple exponential
smoothing.
Hypothesis test -answer-Statistical test to determine the probability that a property of a
sample of data is true for the whole population.
iid -answer-Independent and identically distributed.
Improving direction -answer-Vector of changes to a solution to an optimization problem,
such that the objective function gets better when moving the solution some distance in
the vector's direction.
Imputation -answer-Inserting values where data is missing
Independent -answer-A is "independent" of B if the probability or probability distribution
of A is not affected by B. For example, whether a coin flip is heads or tails is (I assume)
independent of the number of fish in the ocean exactly 100 years ago to this day, but
the temperature today is not independent of the temperature yesterday (if it was hot
yesterday, it's more likely to be hot today too, etc.).
,Independent and identically distributed (iid) -answer-Things that follow the same
probability distribution, including the same parameter(s), and whose values are
independent of each other. For example, multiple flips of the same coin are iid.
Infinity-norm -answer-Specific case of p-norm when 𝑝𝑝 = ∞. Sounds weird, but it just
reduces to the largest of the dimensions. If 𝑧𝑧 = (𝑧𝑧1, 𝑧𝑧2, ... , 𝑧𝑧𝑚𝑚) is a vector in an
𝑚𝑚-dimensional space, then its ∞-norm is max𝑖𝑖|𝑧𝑧𝑖𝑖|. If 𝑥𝑥 = (𝑥𝑥1, 𝑥𝑥2, ... , 𝑥𝑥𝑚𝑚) and
𝑦𝑦 = (𝑦𝑦1, 𝑦𝑦2, ... , 𝑦𝑦𝑚𝑚) are two points in an 𝑚𝑚- dimensional space, then the ∞-
norm distance between them is max𝑖𝑖|𝑥𝑥𝑖𝑖 − 𝑦𝑦𝑖𝑖|.
Initialization -answer-Setting starting values in an algorithm, or setting the first solution
value for an "direction/step-size" optimization algorithm.
Integer program -answer-Optimization model where the objective function is a linear
function of the variables, the constraints are linear equations and/or linear inequalities in
terms of the variables, and some or all variables are restricted to have integer values.
Interaction term -answer-Variable in a model that is the combination of two or more
other variables; for example, if 𝑥𝑥1 and 𝑥𝑥2 are variables, (𝑥𝑥1𝑥𝑥2) is an interaction
term/interaction variable.
Interarrival time -answer-The time between two consecutive arrivals of people, things,
etc. -- for example, the time between consecutive phone calls to a service hotline.
Iterate -answer-Repeat the same steps of a process.
k-fold cross-validation -answer-Validation technique where data is divided into several
parts ("folds"), and each part is used to validate a model fit to the remaining parts. Often
a more robust validation approach than splitting data into training and validation sets.
𝑘-means algorithm -answer-Clustering algorithm that defines 𝑘𝑘 clusters of data points,
each corresponding to one of 𝑘𝑘 cluster centers selected by the algorithm.
𝑘𝑘-Nearest-Neighbor (KNN) -answer-Classification algorithm that defines a data point's
category as a function of the nearest 𝑘𝑘 data points to it.
𝑘𝑘-Nearest-Neighbor regression -answer-Regression model where a data point's
response is estimated based on the responses of the 𝑘𝑘 nearest data points with known
response.
Kendall notation -answer-Notation to describe various types of queuing models -- for
example, M/M/c (a queue with Poisson arrivals, exponentially-distributed service times,
and c identical servers)
, Kernel -answer-A type of function that computes the similarity between two inputs;
thanks to what's (really!) sometimes known as the "kernel trick", nonlinear classifiers
can be found almost as easily as linear ones.
Knot -answer-Point where pieces of a spline regression meet.
Lasso/Lasso regression -answer-Method for limiting the number of variables in a model
by limiting the sum of all coefficients' absolute values. Can be very helpful when number
of data points is less than number of factors.
Leaf -answer-n a tree model, a subset of data from which there is no branching.
Learning -answer-Finding/discovering patterns (or rules) in data, often that can be
applied to new data.
LIFO -answer-Last-in, first-out: The last entity to join a queue is the first one to come
out -- for example, a stack of papers.
Likelihood -answer-Probability that a model with specific parameter values would
generate the actual outcomes in the data.
Linear combination -answer-The weighted sum of things. For example, if 𝑥𝑥1, 𝑥𝑥2, ... ,
𝑥𝑥𝑚𝑚 are factors, then 𝑎𝑎1𝑥𝑥1 + 𝑎𝑎2𝑥𝑥2 + ⋯ + 𝑎𝑎𝑚𝑚𝑥𝑥𝑚𝑚 is a weighted sum of them
for any numbers 𝑎𝑎1, 𝑎𝑎2, ... , 𝑎𝑎𝑚𝑚.
Linear equation -answer-Equation where a linear function is set equal to a constant or
another linear function.
Linear function -answer-Weighted sum of variables, plus a constant: 𝑎𝑎0 + ∑ 𝑎𝑎𝑖𝑖𝑥𝑥𝑖𝑖
𝑚𝑚 𝑖𝑖=1 .
Linear inequality -answer-Equation where a linear function is set equal to a constant or
another linear function.
Linear function -answer-Weighted sum of variables, plus a constant: 𝑎𝑎0 + ∑ 𝑎𝑎𝑖𝑖𝑥𝑥𝑖𝑖
𝑚𝑚 𝑖𝑖=1 .
Linear inequality -answer-Inequality where a linear function is set to be greater-than-or-
equal-to or less-than-or-equal-to a constant or another linear function.
Linear program -answer-An mathematical programming model where the objective
function is a linear function of the variables, and the constraints are linear equations
and/or linear inequalities in terms of the variables.
