Description Usage Arguments Value Examples
View source: R/define_penalty.R
Defines regularization for predictors and external data variables in xrnet
fitting.
Use helper functions define_lasso, define_ridge, or define_enet to specify a common penalty on x or external.
1 2 3 4 5 6 7 8 | define_penalty(
penalty_type = 1,
quantile = 0.5,
num_penalty = 20,
penalty_ratio = NULL,
user_penalty = NULL,
custom_multiplier = NULL
)
|
penalty_type |
type of regularization. Default is 1 (Lasso). Can supply either a scalar value or vector with length equal to the number of variables the matrix.
|
quantile |
specifies quantile for quantile penalty. Default of 0.5 reduces to lasso (currently not implemented). |
num_penalty |
number of penalty values to fit in grid. Default is 20. |
penalty_ratio |
ratio between minimum and maximum penalty for x. Default is 1e-04 if n > p and 0.01 if n <= p. |
user_penalty |
user-defined vector of penalty values to use in penalty path. |
custom_multiplier |
variable-specific penalty multipliers to apply to overall penalty. Default is 1 for all variables. 0 is no penalization. |
A list object with regularization settings that are used to define the regularization
for predictors or external data in xrnet
and tune_xrnet
:
penalty_type |
The penalty type, scalar with value in range [0, 1]. |
quantile |
Quantile for quantile penalty, 0.5 defaults to lasso (not currently implemented). |
num_penalty |
The number of penalty values in the penalty path. |
penalty_ratio |
The ratio of the minimum penalty value compared to the maximum penalty value. |
user_penalty |
User-defined numeric vector of penalty values, NULL if not provided by user. |
custom_multiplier |
User-defined feature-specific penalty multipliers, NULL if not provided by user. |
1 2 3 4 5 | # define ridge penalty with penalty grid split into 30 values
my_penalty <- define_penalty(penalty_type = 0, num_penalty = 30)
# define elastic net (0.5) penalty with user-defined penalty
my_custom_penalty <- define_penalty(penalty_type = 0.5, user_penalty = c(100, 50, 10, 1, 0.1))
|
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