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ISYE 6501 Final Exam Study Guide Latest Updated Georgia Institute of Technology
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ISYE 6501 Final Exam Study Guide Latest Updated Georgia Institute of Technology
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ISYE 6501 Final Exam Study Guide Latest Updated Georgia Institute ofTechnology
1. Factor Based classification, clustering, regression. Implicitly assumed
Models that we have a lot of factors in the final model
2. Why limit num- overfitting: when # of factors is close to or larger than # of
ber of factors in data points. Model may fit too closely to random effects
a model? 2 rea- simplicity: simple models are usually better
sons
3. Classical vari- 1. Forward selection
able selection ap- 2. Backwards elimination
proaches 3. Stepwise regression
greedy algorithms
4. Backward elimi- variable selection; classical
nation Opposite of forward selection. Start with model with all
factors, at each step find worst factor and remove from
model. Continue until no more to add, # of factor threshold
is satisfied. Remove factors at the end that were not good
enough
5. Forward selec- variable selection; classical
tion Start with model with no factors, at each step find best new
factor to add. Continue until none bad enough to remove,
# of factor threshold is satisfied. Remove factors at the end
that were not good enough
6. Stepwise regres- variable selection; classical
sion Combination of forward selection and backwards elimina-
tion. Start with all or no factors. Each step remove/add
a factor. As it continues, after adding in new factor we
eliminate right away any factors that may be good. Helps
model adjust when new factors are added, goodness val-
ues change
7. Ways of deter- p-value, Rsquared, AIC, BIC
mining if factors
are good enough
in variable selec-
tion
, ISYE 6501 Final Exam Study Guide Latest Updated Georgia Institute of
Technology
8. Greedy algorithm At each step, it does the one thing that looks best
without taking future options into consideration. Good for
initial analysis
1. Forward selection
2. Backwards elimination
3. Stepwise regression
9. Global variable 1. LASSO
selection ap- 2. Elastic Net
proaches
Slower, but tend to give better predictive models
10. LASSO variable selection; global
- SCALE the date (as with any constrained sum of coeffi-
cients)
- add a constraint to the standard regression equation
- minimize sum of squared errors
- T = limit or "budget" on how large the sum of squared
errors can get. Budget will be used on most important
coefficients
- 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.
11. Elastic Net variable selection; global
- SCALE the date (as with any constrained sum of coeffi-
cients)
- T = limit or "budget" on how large the sum of squared
errors can get. Budget will be used on most important
coefficients
- Combination of lasso and ridge regression.
- Variable selection benefits of LASSO
- Predictive benefits of ridge regression
12. Ridge Regres- - Method of regularization by limiting the sum of the
sion squares of the coefficients. Will reduce the magnitude of
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