ISYE 6501 Midterm 2 Terms
Exam
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, 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.
Overfitting Building a model that describes random effects instead of or in significant
addition to the real effects; often caused by having too many factors or
parameters compared to the number of data points. ____________ models will have
high prediction errors.
Regularization Addition of term(s) to the model to reduce model complexity or overfitting. For
example, adding a penalty to the objective function in regression can help
reduce overfitting (see ridge regression).
Ridge regression Method of regularization by limiting the sum of the squares of the coefficients.
Will reduce the magnitude of coefficients, not the number of variables chosen.
Simplicity (of a model) Having fewer parameters; opposite of complexity of a model. Often helpful for
avoiding overfitting and increasing interpretability.
Stepwise regression Variable selection process that can combine forward selection and backward
regression.
Variable selection Process of selecting the best subset of predictors to explain variance in data;
involves eliminating unnecessary or redundant or less-important variables from
a potential set of predictors.
A/B testing Test of two alternatives to see if either one leads to better outcomes.
Analysis of Variance/ANOVA Statistical method for dividing the variation in observations among different
sources.
Balanced design Set of combinations of factor values across multiple factors, that has the same
number of runs for all combinations of levels of one or more factors.
Blocking Factor introduced to an experimental design that interacts with the effect of the
factors to be studied. The effect of the factors is studied within the same level
(block) of the blocking factor.
Exam
** Expert-Verified Explanation
** Questions with Verified Answer
** New Edition | 2026-2027 Updated
** 100% Guaranteed Pass
** 100% Correct Answers
, 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.
Overfitting Building a model that describes random effects instead of or in significant
addition to the real effects; often caused by having too many factors or
parameters compared to the number of data points. ____________ models will have
high prediction errors.
Regularization Addition of term(s) to the model to reduce model complexity or overfitting. For
example, adding a penalty to the objective function in regression can help
reduce overfitting (see ridge regression).
Ridge regression Method of regularization by limiting the sum of the squares of the coefficients.
Will reduce the magnitude of coefficients, not the number of variables chosen.
Simplicity (of a model) Having fewer parameters; opposite of complexity of a model. Often helpful for
avoiding overfitting and increasing interpretability.
Stepwise regression Variable selection process that can combine forward selection and backward
regression.
Variable selection Process of selecting the best subset of predictors to explain variance in data;
involves eliminating unnecessary or redundant or less-important variables from
a potential set of predictors.
A/B testing Test of two alternatives to see if either one leads to better outcomes.
Analysis of Variance/ANOVA Statistical method for dividing the variation in observations among different
sources.
Balanced design Set of combinations of factor values across multiple factors, that has the same
number of runs for all combinations of levels of one or more factors.
Blocking Factor introduced to an experimental design that interacts with the effect of the
factors to be studied. The effect of the factors is studied within the same level
(block) of the blocking factor.
