ISYE 6501 CORRECT FINALS QUESTIONS AND
ANSWERS SET A+
✔✔Hit rate - ✔✔Fraction of data points in a certain category that are correctly classified
by a model; equal to TP / TP+FN; also called the true positive rate, sensitivity, and
recall.
✔✔Holt-Winters method/Winters' method - ✔✔Three-parameter exponential smoothing
technique that incorporates trend and seasonality; also called triple exponential
smoothing.
✔✔Hypothesis test - ✔✔Statistical test to determine the probability that a property of a
sample of data is true for the whole population.
✔✔iid (Independent and identically distributed.) - ✔✔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.
✔✔Improving direction - ✔✔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 - ✔✔Inserting values where data is missing.
✔✔Independent - ✔✔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.).
✔✔Infinity norm - ✔✔Specific case of p-norm when 𝑝=∞. Sounds weird, but it just
reduces to the largest of the dimensions. If 𝑧=(𝑧1,𝑧2,...,𝑧𝑚) is a vector in an 𝑚-
,dimensional space, then its ∞-norm is max𝑖|𝑧𝑖|. If 𝑥=(𝑥1,𝑥2,...,𝑥𝑚) and 𝑦=(𝑦1,𝑦2,...,𝑦𝑚)
are two points in an 𝑚-dimensional space, then the ∞-norm distance between them is
max𝑖|𝑥𝑖−𝑦𝑖|.
✔✔Initialization - ✔✔Setting starting values in an algorithm, or setting the first solution
value for an "direction/step-size" optimization algorithm.
✔✔Integer program - ✔✔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 - ✔✔Variable in a model that is the combination of two or more
other variables; for example, if 𝑥1 and 𝑥2 are variables, (𝑥1𝑥2) is an interaction
term/interaction variable.
✔✔interarrival times - ✔✔The time between two consecutive arrivals of people, things,
etc. -- for example, the time between consecutive phone calls to a service hotline.
✔✔Iterate - ✔✔Repeat the same steps of a process.
✔✔k-fold cross validation - ✔✔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 - ✔✔Clustering algorithm that defines 𝑘 clusters of data points,
each corresponding to one of 𝑘 cluster centers selected by the algorithm.
✔✔K-Nearest Neighbor - ✔✔Classification algorithm that defines a data point's category
as a function of the nearest 𝑘 data points to it.
✔✔k-nearest neighbor regression - ✔✔Regression model where a data point's
response is estimated based on the responses of the 𝑘 nearest data points with known
response.
✔✔Kendall Notation - ✔✔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 - ✔✔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 - ✔✔Point where pieces of a spline regression meet.
, ✔✔Lasso/Lasso regression - ✔✔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 - ✔✔In a tree model, a subset of data from which there is no branching.
✔✔Learning - ✔✔Finding/discovering patterns (or rules) in data, often that can be
applied to new data.
✔✔LIFO - ✔✔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 - ✔✔Probability that a model with specific parameter values would
generate the actual outcomes in the data.
✔✔Linear Combination - ✔✔The weighted sum of things. For example, if 𝑥1,𝑥2,...,𝑥𝑚
are factors, then 𝑎1𝑥1+𝑎2𝑥2+⋯+𝑎𝑚𝑥𝑚 is a weighted sum of them for any numbers
𝑎1,𝑎2,...,𝑎𝑚.
✔✔Linear Equation - ✔✔Equation where a linear function is set equal to a constant or
another linear function.
✔✔Linear function - ✔✔Weighted sum of variables, plus a constant: 𝑎0+Σm over i=1
𝑎𝑖𝑥𝑖
✔✔linear inequality - ✔✔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 - ✔✔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.
✔✔linear regression - ✔✔Regression model where the relationships between attributes
and a response are modeled as linear functions: 𝑦=𝑎0+Σ m over i=1 𝑎𝑖𝑥𝑖
✔✔Local optimum/maximum/minimum - ✔✔A solution that achieves a better objective
value than any feasible solutions that are close to it; sometimes also used to refer to
that solution's objective value.
✔✔Logistic regression - ✔✔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: y= +𝑒^-(a0+Σ m over i=1 𝑎𝑖𝑥𝑖). Also called a logit model.
ANSWERS SET A+
✔✔Hit rate - ✔✔Fraction of data points in a certain category that are correctly classified
by a model; equal to TP / TP+FN; also called the true positive rate, sensitivity, and
recall.
✔✔Holt-Winters method/Winters' method - ✔✔Three-parameter exponential smoothing
technique that incorporates trend and seasonality; also called triple exponential
smoothing.
✔✔Hypothesis test - ✔✔Statistical test to determine the probability that a property of a
sample of data is true for the whole population.
✔✔iid (Independent and identically distributed.) - ✔✔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.
✔✔Improving direction - ✔✔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 - ✔✔Inserting values where data is missing.
✔✔Independent - ✔✔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.).
✔✔Infinity norm - ✔✔Specific case of p-norm when 𝑝=∞. Sounds weird, but it just
reduces to the largest of the dimensions. If 𝑧=(𝑧1,𝑧2,...,𝑧𝑚) is a vector in an 𝑚-
,dimensional space, then its ∞-norm is max𝑖|𝑧𝑖|. If 𝑥=(𝑥1,𝑥2,...,𝑥𝑚) and 𝑦=(𝑦1,𝑦2,...,𝑦𝑚)
are two points in an 𝑚-dimensional space, then the ∞-norm distance between them is
max𝑖|𝑥𝑖−𝑦𝑖|.
✔✔Initialization - ✔✔Setting starting values in an algorithm, or setting the first solution
value for an "direction/step-size" optimization algorithm.
✔✔Integer program - ✔✔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 - ✔✔Variable in a model that is the combination of two or more
other variables; for example, if 𝑥1 and 𝑥2 are variables, (𝑥1𝑥2) is an interaction
term/interaction variable.
✔✔interarrival times - ✔✔The time between two consecutive arrivals of people, things,
etc. -- for example, the time between consecutive phone calls to a service hotline.
✔✔Iterate - ✔✔Repeat the same steps of a process.
✔✔k-fold cross validation - ✔✔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 - ✔✔Clustering algorithm that defines 𝑘 clusters of data points,
each corresponding to one of 𝑘 cluster centers selected by the algorithm.
✔✔K-Nearest Neighbor - ✔✔Classification algorithm that defines a data point's category
as a function of the nearest 𝑘 data points to it.
✔✔k-nearest neighbor regression - ✔✔Regression model where a data point's
response is estimated based on the responses of the 𝑘 nearest data points with known
response.
✔✔Kendall Notation - ✔✔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 - ✔✔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 - ✔✔Point where pieces of a spline regression meet.
, ✔✔Lasso/Lasso regression - ✔✔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 - ✔✔In a tree model, a subset of data from which there is no branching.
✔✔Learning - ✔✔Finding/discovering patterns (or rules) in data, often that can be
applied to new data.
✔✔LIFO - ✔✔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 - ✔✔Probability that a model with specific parameter values would
generate the actual outcomes in the data.
✔✔Linear Combination - ✔✔The weighted sum of things. For example, if 𝑥1,𝑥2,...,𝑥𝑚
are factors, then 𝑎1𝑥1+𝑎2𝑥2+⋯+𝑎𝑚𝑥𝑚 is a weighted sum of them for any numbers
𝑎1,𝑎2,...,𝑎𝑚.
✔✔Linear Equation - ✔✔Equation where a linear function is set equal to a constant or
another linear function.
✔✔Linear function - ✔✔Weighted sum of variables, plus a constant: 𝑎0+Σm over i=1
𝑎𝑖𝑥𝑖
✔✔linear inequality - ✔✔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 - ✔✔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.
✔✔linear regression - ✔✔Regression model where the relationships between attributes
and a response are modeled as linear functions: 𝑦=𝑎0+Σ m over i=1 𝑎𝑖𝑥𝑖
✔✔Local optimum/maximum/minimum - ✔✔A solution that achieves a better objective
value than any feasible solutions that are close to it; sometimes also used to refer to
that solution's objective value.
✔✔Logistic regression - ✔✔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: y= +𝑒^-(a0+Σ m over i=1 𝑎𝑖𝑥𝑖). Also called a logit model.
