GLM¶
Generalized Linear Model.
This serves as a base class for linear and logistic regression.
Parameters¶
-
optimizer
The sequential optimizer used for updating the weights. Note that the intercept updates are handled separately.
-
loss
The loss function to optimize for.
-
l2
Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.
-
l1
Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.
-
intercept_init
Initial intercept value.
-
intercept_lr
Learning rate scheduler used for updating the intercept. A
optim.schedulers.Constant
is used if afloat
is provided. The intercept is not updated when this is set to 0. -
clip_gradient
Clips the absolute value of each gradient value.
-
initializer
Weights initialization scheme.
Attributes¶
- weights