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Elastic Net
Elastic Net
3 questions
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state theorem
Elastic Net regression combines two penalty terms. Which?
Hide and think first
A.
ℓ
0
(counting penalty) and
ℓ
∞
(max absolute value)
B.
ℓ
1
(Lasso) and
ℓ
2
(Ridge):
λ
1
∥
β
∥
1
+
λ
2
∥
β
∥
2
2
C.
Dropout probability and weight decay, both tuned simultaneously
D.
Huber loss and KL divergence to a prior distribution on coefficients
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