View source: R/patient-level_modeling.R
tof_create_grid | R Documentation |
This function creates a regular hyperparameter search grid (in the form of a
tibble
) specifying the search space for the two
hyperparameters of a generalized linear model using the glmnet package:
the regularization penalty term
and the lasso/ridge regression mixture term.
tof_create_grid(
penalty_values,
mixture_values,
num_penalty_values = 5,
num_mixture_values = 5
)
penalty_values |
A numeric vector of the unique elastic net penalty values ("lambda") to include in the hyperparameter grid. If unspecified, a regular grid with 'num_penalty_values' between 10^(-10) and 10^(0) will be used. |
mixture_values |
A numeric vector of all elastic net mixture values ("alpha") to include in the hyperparameter grid. If unspecified, a regular grid with 'num_mixture_values' between 0 and 1 will be used. |
num_penalty_values |
Optional. If 'penalty_values' is not supplied, 'num_penalty_values' (an integer) can be given to specify how many equally-spaced penalty values between 10^(-10) and 1 should be included in the hyperparameter grid. If this method is used, the regular grid will always be returned. Defaults to 5. |
num_mixture_values |
Optional. If 'mixture_values' is not supplied, 'num_mixture_values' (an integer) can be given to specify how many equally-spaced penalty values between 0 (ridge regression) and 1 (lasso) should be included in the hyperparameter grid. If this method is used, the regular grid will always be returned. Defaults to 5. |
A tibble with two numeric columns: 'penalty' and 'mixture'.
Other modeling functions:
tof_assess_model()
,
tof_predict()
,
tof_split_data()
,
tof_train_model()
tof_create_grid()
tof_create_grid(num_penalty_values = 10, num_mixture_values = 5)
tof_create_grid(penalty_values = c(0.01, 0.1, 0.5))
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