For this engine, there is a single mode: regression
This model has one tuning parameter:
penalty
: Amount of Regularization (type: double, default: 0.0)For penalty
, the amount of regularization is only L2 penalty (i.e., ridge or weight decay).
linear_reg(penalty = double(1)) %>%
set_engine("keras") %>%
translate()
## Linear Regression Model Specification (regression)
##
## Main Arguments:
## penalty = double(1)
##
## Computational engine: keras
##
## Model fit template:
## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), penalty = double(1),
## hidden_units = 1, act = "linear")
[keras_mlp()] is a parsnip wrapper around keras code for neural networks. This model fits a linear regression as a network with a single hidden unit.
Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.
The underlying model implementation does not allow for case weights.
The "Fitting and Predicting with parsnip" article contains examples for linear_reg()
with the "keras"
engine.
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