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ISYE 6501 FINAL
EXAM|COMPREHENSIVE Q&A FOR
CERTIFICATION SUCCESS
GARCH (Generalized Autoregressive Conditional
Heteroskedasticity) - correct-answer - Time series analytic method that
estimates/forecasts variance. Helps determine how much a forecast may be
higher or lower than the true value. Useful for estimating risks on investment
portfolios.
Linear Regression - correct-answer - A Regression technique that describes
relationships between independent and dependent variables as linear functions
AIC (Akaike information criterion) - correct-answer - Model selection technique
that balances model fit and complexity. Penalizes models with too much
complexity in an attempt to avoid overfitting.
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BIC (Bayesian information criterion) - correct-answer - Model election technique
that balances model fit and complexity. Generally penalizes complexity more than
AIC.
Box-Cox Transformation - correct-answer - Logarithmic transformation technique
used to eliminate heteroskedasticity (unequal variance) across a data set to make
it fit a normal distribution. This will help a model avoid being biased towards parts
of the data with greater variance.
PCA (principal component analysis) - correct-answer - Feature extraction
technique that can reduce the dimensionality of the feature space in a model,
creating new components ordered by importance, while also transforming the
data around new orthogonal axis in order to remove correlation.
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PCA Pros and Cons - correct-answer - Pros: Removes correlated features
Can improve algorithm performance by omitting correlated variables
Reduces over fitting
Improves visualization
Cons: Harder to interpret effects of individual independent variables
Requires data standardization
CART (classification and regression trees) - correct-answer - Regression technique
that creates different models for different subset of data. This is useful when
different factors may have different ways they interact that makes them more
effective to model separately. For example when modeling likeliness of response
to advertisement, may be useful to divide population into age group and marital
status.
Branching - correct-answer - The process of deciding how to split the data, and
which factors to include in a CART model. Can go either backwards, removing
leaves that don't decrease variance more than a certain threshold, or forward by
ISYE 6501 FINAL
EXAM|COMPREHENSIVE Q&A FOR
CERTIFICATION SUCCESS
GARCH (Generalized Autoregressive Conditional
Heteroskedasticity) - correct-answer - Time series analytic method that
estimates/forecasts variance. Helps determine how much a forecast may be
higher or lower than the true value. Useful for estimating risks on investment
portfolios.
Linear Regression - correct-answer - A Regression technique that describes
relationships between independent and dependent variables as linear functions
AIC (Akaike information criterion) - correct-answer - Model selection technique
that balances model fit and complexity. Penalizes models with too much
complexity in an attempt to avoid overfitting.
,2|Page
BIC (Bayesian information criterion) - correct-answer - Model election technique
that balances model fit and complexity. Generally penalizes complexity more than
AIC.
Box-Cox Transformation - correct-answer - Logarithmic transformation technique
used to eliminate heteroskedasticity (unequal variance) across a data set to make
it fit a normal distribution. This will help a model avoid being biased towards parts
of the data with greater variance.
PCA (principal component analysis) - correct-answer - Feature extraction
technique that can reduce the dimensionality of the feature space in a model,
creating new components ordered by importance, while also transforming the
data around new orthogonal axis in order to remove correlation.
, 3|Page
PCA Pros and Cons - correct-answer - Pros: Removes correlated features
Can improve algorithm performance by omitting correlated variables
Reduces over fitting
Improves visualization
Cons: Harder to interpret effects of individual independent variables
Requires data standardization
CART (classification and regression trees) - correct-answer - Regression technique
that creates different models for different subset of data. This is useful when
different factors may have different ways they interact that makes them more
effective to model separately. For example when modeling likeliness of response
to advertisement, may be useful to divide population into age group and marital
status.
Branching - correct-answer - The process of deciding how to split the data, and
which factors to include in a CART model. Can go either backwards, removing
leaves that don't decrease variance more than a certain threshold, or forward by
