ISYE6501 / ISYE 6501 Midterm Exam 2 (Latest
Update ) Intro to Analytics
Modeling | Questions & Answers | Grade A |
100% Correct. Georgia Tech
1. Backward elimination: Variable selection process that starts with all
variables and then iteratively removes the least-immediately-relevant
variables from the model.
2. Elastic net: Combination of lasso and ridge regression.
3. Forward selection: Variable selection process that starts with no variables
and then iteratively adds the most-immediately-relevant variables to the
model.
4. 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.
5. Overfitting: Building a model that describes random effects instead of or
in significant addition to the real effects; often caused by having too many
1
, factors or parameters compared to the number of data points.
____________ models will have high prediction errors.
6. 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).
7. 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.
8. Simplicity (of a model): Having fewer parameters; opposite of
complexity of a model. Often helpful for avoiding overfitting and
increasing interpretability.
9. Stepwise regression: Variable selection process that can combine forward
selection and backward regression.
10. 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.
11. A/B testing: Test of two alternatives to see if either one leads to better
outcomes.
, 12. Analysis of Variance/ANOVA: Statistical method for dividing the
variation in observations among different sources.
13. 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.
14. 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.
15. Control: (1) A variable whose value remains constant for all runs of an
experiment, so changes in this variable don't affect the experiment. (2) Design
an experiment where some factors ("controls" by definition (1)) are held
constant to avoid them affecting the outcome.
16 Design of experiments: Choosing a set of tests to be made to find the
effect of input variables on an outcome.
17. Exploitation: Using known information to get good outcomes.
18. Exploration: Finding new/better/more information to determine how to
optimize output.
3
Update ) Intro to Analytics
Modeling | Questions & Answers | Grade A |
100% Correct. Georgia Tech
1. Backward elimination: Variable selection process that starts with all
variables and then iteratively removes the least-immediately-relevant
variables from the model.
2. Elastic net: Combination of lasso and ridge regression.
3. Forward selection: Variable selection process that starts with no variables
and then iteratively adds the most-immediately-relevant variables to the
model.
4. 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.
5. Overfitting: Building a model that describes random effects instead of or
in significant addition to the real effects; often caused by having too many
1
, factors or parameters compared to the number of data points.
____________ models will have high prediction errors.
6. 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).
7. 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.
8. Simplicity (of a model): Having fewer parameters; opposite of
complexity of a model. Often helpful for avoiding overfitting and
increasing interpretability.
9. Stepwise regression: Variable selection process that can combine forward
selection and backward regression.
10. 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.
11. A/B testing: Test of two alternatives to see if either one leads to better
outcomes.
, 12. Analysis of Variance/ANOVA: Statistical method for dividing the
variation in observations among different sources.
13. 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.
14. 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.
15. Control: (1) A variable whose value remains constant for all runs of an
experiment, so changes in this variable don't affect the experiment. (2) Design
an experiment where some factors ("controls" by definition (1)) are held
constant to avoid them affecting the outcome.
16 Design of experiments: Choosing a set of tests to be made to find the
effect of input variables on an outcome.
17. Exploitation: Using known information to get good outcomes.
18. Exploration: Finding new/better/more information to determine how to
optimize output.
3
