Setup hyperparameters for GLMNET training.
Usage
setup_GLMNET(
alpha = 1,
family = NULL,
offset = NULL,
which_lambda_cv = "lambda.1se",
nlambda = 100L,
lambda = NULL,
penalty_factor = NULL,
standardize = TRUE,
intercept = TRUE,
ifw = TRUE
)Arguments
- alpha
(Tunable) Numeric [0, 1]: Elastic net mixing parameter.
- family
Optional Character {"gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"}: Family. NULL = set from outcome type.
- offset
Optional Numeric vector: Offset, one value per case.
- which_lambda_cv
Character {"lambda.1se", "lambda.min"}: Which lambda to use for prediction.
- nlambda
Integer [1, Inf): Number of lambda values.
- lambda
Optional Numeric [0, Inf) vector: Lambda values. NULL = determined by cv.glmnet during tuning.
- penalty_factor
Optional Numeric [0, Inf) vector: Penalty factor for each feature.
- standardize
Logical: If TRUE, standardize features.
- intercept
Logical: If TRUE, include intercept.
- ifw
(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.
Details
Get more information from glmnet::glmnet.
Examples
glm_hyperparams <- setup_GLMNET(alpha = 1, ifw = TRUE)
glm_hyperparams
#> <GLMNETHyperparameters>
#> hyperparameters:
#> alpha: <nmr> 1.00
#> family: <NUL> NULL
#> offset: <NUL> NULL
#> which_lambda_cv: <chr> lambda.1se
#> nlambda: <int> 100
#> lambda: <NUL> NULL
#> penalty_factor: <NUL> NULL
#> standardize: <lgc> TRUE
#> intercept: <lgc> TRUE
#> ifw: <lgc> TRUE
#> lambda.min: <NUL> NULL
#> lambda.1se: <NUL> NULL
#> tunable_hyperparameters: <chr> alpha, ifw
#> fixed_hyperparameters: <chr> family, offset, which_lambda_cv, nlambda, lambda, penalty_factor, standardize, intercept, lambda.min, lambda.1se
#> tuned: <int> 0
#> resampled: <int> 0
#> n_workers: <int> 1
#>
#> Hyperparameter lambda needs tuning.