when might overfitting occur
when the # of factors is close to or larger than the # of data points causing the model to potentially fit too
closely to random effects
Why are simple models better than complex ones
less data is required; less chance of insignificant factors and easier to interpret
what is forward selection
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
model with the current set of factors. Then at the end we remove factors that are lower than a certain
threshold
,what is backward elimination
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 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
what is stepwise regression
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 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 stepwise selection?
Greedy algorithms - at each step they take one thing that looks best
what is LASSO
a variable selection method where the coefficients are determined by both minimizing the squared error and
the sum of their absolute value not being over a certain threshold t
How do you choose t in LASSO
use the lasso approach with different values of t and see which gives the best trade off
why do we have to scale the data for LASSO
if we don't the measure of the data will artificially affect how big the coefficients need to be
, What is elastic net?
A variable selection method that works by minimizing the squared error and constraining the combination of
absolute values of coefficients and their squares
what is a key difference between stepwise regresson and lasso regression
If the data is not scaled, the coefficients can have artificially different orders of magnitude, which means they'll
have unbalanced effects on the lasso constraint.
Why doesn't Ridge Regression perform variable selection?
The coefficients values are squared so they go closer to zero or regularizes them
when the # of factors is close to or larger than the # of data points causing the model to potentially fit too
closely to random effects
Why are simple models better than complex ones
less data is required; less chance of insignificant factors and easier to interpret
what is forward selection
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
model with the current set of factors. Then at the end we remove factors that are lower than a certain
threshold
,what is backward elimination
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 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
what is stepwise regression
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 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 stepwise selection?
Greedy algorithms - at each step they take one thing that looks best
what is LASSO
a variable selection method where the coefficients are determined by both minimizing the squared error and
the sum of their absolute value not being over a certain threshold t
How do you choose t in LASSO
use the lasso approach with different values of t and see which gives the best trade off
why do we have to scale the data for LASSO
if we don't the measure of the data will artificially affect how big the coefficients need to be
, What is elastic net?
A variable selection method that works by minimizing the squared error and constraining the combination of
absolute values of coefficients and their squares
what is a key difference between stepwise regresson and lasso regression
If the data is not scaled, the coefficients can have artificially different orders of magnitude, which means they'll
have unbalanced effects on the lasso constraint.
Why doesn't Ridge Regression perform variable selection?
The coefficients values are squared so they go closer to zero or regularizes them
