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ISYE 6501 - Midterm 2
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ISYE 6501 Final Isye 6501 Final exam CSE 160 Exam 2 Quiz
92 terms 323 terms 58 terms
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when the # of factors is close to or larger than the # of data
when might overfitting occur points causing the model to potentially fit too closely to random
effects
Why are simple models better less data is required; less chance of insignificant factors and
than complex ones easier to interpret
we select the best new factor and see
if it's good enough (R^2, AIC, or p-
value) add it to our model and fit the
what is forward selection
model with the current set of factors.
Then at the end we remove factors
that are lower than a certain threshold
we start with all factors and find the
worst on a supplied threshold (p =
0.15). If it is worse we remove it and
start the process over. We do that until
what is backward elimination
we have the number of factors that we
want and then we move the factors
lower than a second threshold (p = .05)
and fit the model with all set of factors
it is a combination of forward selection
and backward elimination. We can
either start with all factors or no
factors and at each step we remove or
what is stepwise regression add a factor. As we go through the
procedure after adding each new
factor and at the end we eliminate
right away factors that no longer
appear.
what type of algorithms are Greedy algorithms - at each step they take one thing that looks
stepwise selection? best
, a variable selection method where the
coefficients are determined by both
what is LASSO minimizing the squared error and the
sum of their absolute value not being
over a certain threshold t
use the lasso approach with different values of t and see which
How do you choose t in LASSO
gives the best trade off
why do we have to scale the if we don't the measure of the data will artificially affect how big
data for LASSO the coefficients need to be
A variable selection method that works
by minimizing the squared error and
What is elastic net? constraining the combination of
absolute values of coefficients and
their squares
what is a key difference between If the data is not scaled, the coefficients can have artificially
stepwise regresson and lasso different orders of magnitude, which means they'll have
regression unbalanced effects on the lasso constraint.
The coefficients values are squared so
Why doesn't Ridge Regression they go closer to zero or regularizes
perform variable selection? them
What are the pros and cons of Good for initial analysis but often don't perform as well on other
Greedy Algorithms (Forward data because they fit more to random effects than you'd like and
selection, stepwise elimination, appear to have a better fit
stepwise regression)
What are the pros and cons of They are slower but help make models that make better
LASSO and elastic net predictions
Ridge Regression and LASSO.
Advantages: variable selection from LASSO and Predictive
Which two methods does elastic benefits of LASSO.
net look like it combines and
what are the downsides from it? Disadvantages: Arbitrarily rules out some correlated variables
like LASSO (don't know which one that is left out should be);
Underestimates coefficients of very predictive variables like
Ridge Regresison
What are some downsides of Even if you what appears to be a representative sample in simple
surveys? ways, maybe it isn't in more complex ways.
If we're testing to see whether Controlling
red cars sell for higher prices
than blue cars, we need to
account for the type and age of
the cars in our data set. This is
called:
a source of variability that is not of primary interest to the
what is a blocking factor
experimenter
ISYE 6501 - Midterm 2
Save
Students also studied
Flashcard sets Study guides
ISYE 6501 Final Isye 6501 Final exam CSE 160 Exam 2 Quiz
92 terms 323 terms 58 terms
Brent_Ducote Preview Kayla_Patten2 Preview Sinan_Aksu
when the # of factors is close to or larger than the # of data
when might overfitting occur points causing the model to potentially fit too closely to random
effects
Why are simple models better less data is required; less chance of insignificant factors and
than complex ones easier to interpret
we select the best new factor and see
if it's good enough (R^2, AIC, or p-
value) add it to our model and fit the
what is forward selection
model with the current set of factors.
Then at the end we remove factors
that are lower than a certain threshold
we start with all factors and find the
worst on a supplied threshold (p =
0.15). If it is worse we remove it and
start the process over. We do that until
what is backward elimination
we have the number of factors that we
want and then we move the factors
lower than a second threshold (p = .05)
and fit the model with all set of factors
it is a combination of forward selection
and backward elimination. We can
either start with all factors or no
factors and at each step we remove or
what is stepwise regression add a factor. As we go through the
procedure after adding each new
factor and at the end we eliminate
right away factors that no longer
appear.
what type of algorithms are Greedy algorithms - at each step they take one thing that looks
stepwise selection? best
, a variable selection method where the
coefficients are determined by both
what is LASSO minimizing the squared error and the
sum of their absolute value not being
over a certain threshold t
use the lasso approach with different values of t and see which
How do you choose t in LASSO
gives the best trade off
why do we have to scale the if we don't the measure of the data will artificially affect how big
data for LASSO the coefficients need to be
A variable selection method that works
by minimizing the squared error and
What is elastic net? constraining the combination of
absolute values of coefficients and
their squares
what is a key difference between If the data is not scaled, the coefficients can have artificially
stepwise regresson and lasso different orders of magnitude, which means they'll have
regression unbalanced effects on the lasso constraint.
The coefficients values are squared so
Why doesn't Ridge Regression they go closer to zero or regularizes
perform variable selection? them
What are the pros and cons of Good for initial analysis but often don't perform as well on other
Greedy Algorithms (Forward data because they fit more to random effects than you'd like and
selection, stepwise elimination, appear to have a better fit
stepwise regression)
What are the pros and cons of They are slower but help make models that make better
LASSO and elastic net predictions
Ridge Regression and LASSO.
Advantages: variable selection from LASSO and Predictive
Which two methods does elastic benefits of LASSO.
net look like it combines and
what are the downsides from it? Disadvantages: Arbitrarily rules out some correlated variables
like LASSO (don't know which one that is left out should be);
Underestimates coefficients of very predictive variables like
Ridge Regresison
What are some downsides of Even if you what appears to be a representative sample in simple
surveys? ways, maybe it isn't in more complex ways.
If we're testing to see whether Controlling
red cars sell for higher prices
than blue cars, we need to
account for the type and age of
the cars in our data set. This is
called:
a source of variability that is not of primary interest to the
what is a blocking factor
experimenter
