setup_approach | R Documentation |
The different choices of approach
takes different (optional) parameters,
which are forwarded from explain()
.
setup_approach(internal, ...)
## S3 method for class 'categorical'
setup_approach(
internal,
categorical.joint_prob_dt = NULL,
categorical.epsilon = 0.001,
...
)
## S3 method for class 'copula'
setup_approach(internal, ...)
## S3 method for class 'ctree'
setup_approach(
internal,
ctree.mincriterion = 0.95,
ctree.minsplit = 20,
ctree.minbucket = 7,
ctree.sample = TRUE,
...
)
## S3 method for class 'empirical'
setup_approach(
internal,
empirical.type = "fixed_sigma",
empirical.eta = 0.95,
empirical.fixed_sigma = 0.1,
empirical.n_samples_aicc = 1000,
empirical.eval_max_aicc = 20,
empirical.start_aicc = 0.1,
empirical.cov_mat = NULL,
model = NULL,
predict_model = NULL,
...
)
## S3 method for class 'gaussian'
setup_approach(internal, gaussian.mu = NULL, gaussian.cov_mat = NULL, ...)
## S3 method for class 'independence'
setup_approach(internal, ...)
## S3 method for class 'regression_separate'
setup_approach(
internal,
regression.model = parsnip::linear_reg(),
regression.tune_values = NULL,
regression.vfold_cv_para = NULL,
regression.recipe_func = NULL,
...
)
## S3 method for class 'regression_surrogate'
setup_approach(
internal,
regression.model = parsnip::linear_reg(),
regression.tune_values = NULL,
regression.vfold_cv_para = NULL,
regression.recipe_func = NULL,
regression.surrogate_n_comb = internal$parameters$used_n_combinations - 2,
...
)
## S3 method for class 'timeseries'
setup_approach(
internal,
timeseries.fixed_sigma_vec = 2,
timeseries.bounds = c(NULL, NULL),
...
)
## S3 method for class 'vaeac'
setup_approach(
internal,
vaeac.depth = 3,
vaeac.width = 32,
vaeac.latent_dim = 8,
vaeac.activation_function = torch::nn_relu,
vaeac.lr = 0.001,
vaeac.n_vaeacs_initialize = 4,
vaeac.epochs = 100,
vaeac.extra_parameters = list(),
...
)
internal |
Not used. |
... |
|
categorical.joint_prob_dt |
Data.table. (Optional)
Containing the joint probability distribution for each combination of feature
values.
|
categorical.epsilon |
Numeric value. (Optional)
If |
ctree.mincriterion |
Numeric scalar or vector. (default = 0.95)
Either a scalar or vector of length equal to the number of features in the model.
Value is equal to 1 - |
ctree.minsplit |
Numeric scalar. (default = 20) Determines minimum value that the sum of the left and right daughter nodes required for a split. |
ctree.minbucket |
Numeric scalar. (default = 7) Determines the minimum sum of weights in a terminal node required for a split |
ctree.sample |
Boolean. (default = TRUE)
If TRUE, then the method always samples |
empirical.type |
Character. (default = |
empirical.eta |
Numeric. (default = 0.95)
Needs to be |
empirical.fixed_sigma |
Positive numeric scalar. (default = 0.1)
Represents the kernel bandwidth in the distance computation used when conditioning on all different combinations.
Only used when |
empirical.n_samples_aicc |
Positive integer. (default = 1000)
Number of samples to consider in AICc optimization.
Only used for |
empirical.eval_max_aicc |
Positive integer. (default = 20)
Maximum number of iterations when optimizing the AICc.
Only used for |
empirical.start_aicc |
Numeric. (default = 0.1)
Start value of the |
empirical.cov_mat |
Numeric matrix. (Optional, default = NULL)
Containing the covariance matrix of the data generating distribution used to define the Mahalanobis distance.
|
model |
Objects.
The model object that ought to be explained.
See the documentation of |
predict_model |
Function.
The prediction function used when |
gaussian.mu |
Numeric vector. (Optional)
Containing the mean of the data generating distribution.
|
gaussian.cov_mat |
Numeric matrix. (Optional)
Containing the covariance matrix of the data generating distribution.
|
regression.model |
A |
regression.tune_values |
Either |
regression.vfold_cv_para |
Either |
regression.recipe_func |
Either |
regression.surrogate_n_comb |
Integer (default is |
timeseries.fixed_sigma_vec |
Numeric. (Default = 2) Represents the kernel bandwidth in the distance computation. TODO: What length should it have? 1? |
timeseries.bounds |
Numeric vector of length two. (Default = c(NULL, NULL)) If one or both of these bounds are not NULL, we restrict the sampled time series to be between these bounds. This is useful if the underlying time series are scaled between 0 and 1, for example. |
vaeac.depth |
Positive integer (default is |
vaeac.width |
Positive integer (default is |
vaeac.latent_dim |
Positive integer (default is |
vaeac.activation_function |
An |
vaeac.lr |
Positive numeric (default is |
vaeac.n_vaeacs_initialize |
Positive integer (default is |
vaeac.epochs |
Positive integer (default is |
vaeac.extra_parameters |
Named list with extra parameters to the |
Martin Jullum
Lars Henry Berge Olsen
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