ISYE 6501 STUDYS QUESTIONS AND ANSWERS SET
A+
✔✔False omission rate - ✔✔Fraction of data points the model classifies as not in a
certain category, that are really in the category; equal to 𝐹𝐹 / 𝑇𝑇+𝐹𝐹.
✔✔Feasible solution - ✔✔A solution that satisfies a set of constraints.
✔✔Feature - ✔✔(1) A characteristic or measurement (2) A combination of attributes in a
specific format
✔✔GARCH - ✔✔Generalized autoregressive conditional heteroscedasticity.
Autoregressive method used to model variance in time series data.
✔✔Heteroscedasticity - ✔✔When the variability of a response is different across the
range of predictor values.
✔✔Heuristic - ✔✔Algorithm that is not guaranteed to find the absolute best (optimal)
solution.
✔✔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.
✔✔Imputation - ✔✔Inserting values where data is missing.
, ✔✔Independent and identically distributed (iid) 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 - ✔✔Specific case of p-norm when 𝑝𝑝 = ∞. Sounds weird, but it just
reduces to the largest of the dimensions.
✔✔Initialization - ✔✔Setting starting values in an algorithm, or setting the first solution
value for an "direction/step-size" optimization algorithm.
✔✔Interaction term - ✔✔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 - ✔✔The time between two consecutive arrivals of people, things,
etc.
✔✔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.
✔✔𝑘-means algorithm - ✔✔Clustering algorithm that defines 𝑘𝑘 clusters of data points,
each corresponding to one of 𝑘𝑘 cluster centers selected by the algorithm.
✔✔𝑘-Nearest-Neighbor (KNN) Classification algorithm that defines a data point's
category as a function of the nearest 𝑘𝑘 data points to it. - ✔✔
✔✔𝑘-Nearest-Neighbor regression - ✔✔Regression model where a data point's
response is estimated based on the responses of the 𝑘𝑘 nearest data points with known
response.
✔✔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.
✔✔Likelihood - ✔✔Probability that a model with specific parameter values would
generate the actual outcomes in the data.
✔✔Linear regression - ✔✔Regression model where the relationships between attributes
and a response are modeled as linear functions: 𝑦 = 𝑎0 + ∑ 𝑎𝑖𝑥𝑖
✔✔Local optimum/maximum/minimum - ✔✔A solution that achieves a better objective
value than any feasible solutions that are close to it
A+
✔✔False omission rate - ✔✔Fraction of data points the model classifies as not in a
certain category, that are really in the category; equal to 𝐹𝐹 / 𝑇𝑇+𝐹𝐹.
✔✔Feasible solution - ✔✔A solution that satisfies a set of constraints.
✔✔Feature - ✔✔(1) A characteristic or measurement (2) A combination of attributes in a
specific format
✔✔GARCH - ✔✔Generalized autoregressive conditional heteroscedasticity.
Autoregressive method used to model variance in time series data.
✔✔Heteroscedasticity - ✔✔When the variability of a response is different across the
range of predictor values.
✔✔Heuristic - ✔✔Algorithm that is not guaranteed to find the absolute best (optimal)
solution.
✔✔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.
✔✔Imputation - ✔✔Inserting values where data is missing.
, ✔✔Independent and identically distributed (iid) 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 - ✔✔Specific case of p-norm when 𝑝𝑝 = ∞. Sounds weird, but it just
reduces to the largest of the dimensions.
✔✔Initialization - ✔✔Setting starting values in an algorithm, or setting the first solution
value for an "direction/step-size" optimization algorithm.
✔✔Interaction term - ✔✔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 - ✔✔The time between two consecutive arrivals of people, things,
etc.
✔✔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.
✔✔𝑘-means algorithm - ✔✔Clustering algorithm that defines 𝑘𝑘 clusters of data points,
each corresponding to one of 𝑘𝑘 cluster centers selected by the algorithm.
✔✔𝑘-Nearest-Neighbor (KNN) Classification algorithm that defines a data point's
category as a function of the nearest 𝑘𝑘 data points to it. - ✔✔
✔✔𝑘-Nearest-Neighbor regression - ✔✔Regression model where a data point's
response is estimated based on the responses of the 𝑘𝑘 nearest data points with known
response.
✔✔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.
✔✔Likelihood - ✔✔Probability that a model with specific parameter values would
generate the actual outcomes in the data.
✔✔Linear regression - ✔✔Regression model where the relationships between attributes
and a response are modeled as linear functions: 𝑦 = 𝑎0 + ∑ 𝑎𝑖𝑥𝑖
✔✔Local optimum/maximum/minimum - ✔✔A solution that achieves a better objective
value than any feasible solutions that are close to it
