ISYE-6501 Exam 1 Questions And Answers With Verified Updates
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ISYE-6501
Data point - Observation/record of (perhaps multiple) measurements for a single member of a
population or data set. In the standard tabular format, a row of data.
Bayesian Information criterion (BIC) - Model selection technique that trades off model fit and
model complexity. When comparing model...
ISYE-6501 Exam 1
Data point - Observation/record of (perhaps multiple) measurements for a single member of a
population or data set. In the standard tabular format, a row of data.
Bayesian Information criterion (BIC) - Model selection technique that trades off model fit and
model complexity. When comparing models, the model with lower BIC is preferred. Generally penalizes
complexity more than AIC.
1-norm (rectilinear distance, Manhattan distance) - Think of distances in blocks E (|xi - yi|)
Accuracy - Fraction of data points correctly classified by the model.
(TP + TN)/(TP + TN + FP + FN)
Additive Seasonality - Seasonal effect that is added to a baseline value (for example, "the
temperature in June is 10 degrees above the annual baseline").
Adjusted R-squared/Adjusted R2 - Variant of R2 that encourages simpler models by penalizing the
use of too many variables.
Akaike information criterion (AIC) - Model selection technique that trades off between model fit
and model complexity. When comparing models, the model with lower AIC is preferred. Generally
penalizes complexity less than BIC.
Algorithm - a step-by-step procedure designed to carry out a task
Area under curve/AUC - Area under the ROC curve; an estimate of the classification model's
accuracy. Also called concordance index.
, Attribute/Covariate/Feature - A characteristic or measurement - for example, a person's height or
the color of a car. Generally interchangeable with "feature", and often with "covariate" or "predictor". In
the standard tabular format, a column of data.
Autoregression - Regression technique using past values of time series data as predictors of future
values.
Autoregressive integrated moving average (ARIMA) - Time series model that uses differences
between observations when data is nonstationary. Also called Box-Jenkins.
Bayesian regression - Regression model that incorporates estimates of how coefficients and error
are distributed.
Binary data - Data that can take only two different values (true/false, 0/1, black/white, on/off,
etc.).
Box and whisker plot - Graphical representation data showing the middle range of data (the
"box"), reasonable ranges of variability ("whiskers"), and points (possible outliers) outside those ranges.
Box-Cox transformation - Transformation of a non-normally-distributed response to a normal
distribution.
Branching - Splitting a set of data into two or more subsets, to each be analyzed separately.
CART - Classification and regression trees.
Categorical data - Data that classifies observations without quantitative meaning (for example,
colors of cars) or where quantitative amounts are categorized (for example, "0-10, 11-20, ...").
Causation - Relationship in which one thing makes another happen (i.e., one thing causes
another).
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