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Generalized Additive Models
Generalized Additive Models
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Generalized Additive Models (GAMs) extend linear regression. What is their core idea?
Hide and think first
A.
Replace the Gaussian error assumption with a flexible nonparametric error distribution
B.
Use a neural network to fit the regression without any specified functional form
C.
Add polynomial interaction terms across all pairs of features
D.
Replace each linear term
β
j
x
j
with a flexible smooth function
f
j
(
x
j
)
, kept additive across features
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