update.bag_mars  R Documentation 
If parameters of a model specification need to be modified, update()
can
be used in lieu of recreating the object from scratch.
## S3 method for class 'bag_mars'
update(
object,
parameters = NULL,
num_terms = NULL,
prod_degree = NULL,
prune_method = NULL,
fresh = FALSE,
...
)
## S3 method for class 'bag_mlp'
update(
object,
parameters = NULL,
hidden_units = NULL,
penalty = NULL,
epochs = NULL,
fresh = FALSE,
...
)
## S3 method for class 'bag_tree'
update(
object,
parameters = NULL,
cost_complexity = NULL,
tree_depth = NULL,
min_n = NULL,
class_cost = NULL,
fresh = FALSE,
...
)
## S3 method for class 'bart'
update(
object,
parameters = NULL,
trees = NULL,
prior_terminal_node_coef = NULL,
prior_terminal_node_expo = NULL,
prior_outcome_range = NULL,
fresh = FALSE,
...
)
## S3 method for class 'boost_tree'
update(
object,
parameters = NULL,
mtry = NULL,
trees = NULL,
min_n = NULL,
tree_depth = NULL,
learn_rate = NULL,
loss_reduction = NULL,
sample_size = NULL,
stop_iter = NULL,
fresh = FALSE,
...
)
## S3 method for class 'C5_rules'
update(
object,
parameters = NULL,
trees = NULL,
min_n = NULL,
fresh = FALSE,
...
)
## S3 method for class 'cubist_rules'
update(
object,
parameters = NULL,
committees = NULL,
neighbors = NULL,
max_rules = NULL,
fresh = FALSE,
...
)
## S3 method for class 'decision_tree'
update(
object,
parameters = NULL,
cost_complexity = NULL,
tree_depth = NULL,
min_n = NULL,
fresh = FALSE,
...
)
## S3 method for class 'discrim_flexible'
update(
object,
num_terms = NULL,
prod_degree = NULL,
prune_method = NULL,
fresh = FALSE,
...
)
## S3 method for class 'discrim_linear'
update(
object,
penalty = NULL,
regularization_method = NULL,
fresh = FALSE,
...
)
## S3 method for class 'discrim_quad'
update(object, regularization_method = NULL, fresh = FALSE, ...)
## S3 method for class 'discrim_regularized'
update(
object,
frac_common_cov = NULL,
frac_identity = NULL,
fresh = FALSE,
...
)
## S3 method for class 'gen_additive_mod'
update(
object,
select_features = NULL,
adjust_deg_free = NULL,
parameters = NULL,
fresh = FALSE,
...
)
## S3 method for class 'linear_reg'
update(
object,
parameters = NULL,
penalty = NULL,
mixture = NULL,
fresh = FALSE,
...
)
## S3 method for class 'logistic_reg'
update(
object,
parameters = NULL,
penalty = NULL,
mixture = NULL,
fresh = FALSE,
...
)
## S3 method for class 'mars'
update(
object,
parameters = NULL,
num_terms = NULL,
prod_degree = NULL,
prune_method = NULL,
fresh = FALSE,
...
)
## S3 method for class 'mlp'
update(
object,
parameters = NULL,
hidden_units = NULL,
penalty = NULL,
dropout = NULL,
epochs = NULL,
activation = NULL,
learn_rate = NULL,
fresh = FALSE,
...
)
## S3 method for class 'multinom_reg'
update(
object,
parameters = NULL,
penalty = NULL,
mixture = NULL,
fresh = FALSE,
...
)
## S3 method for class 'naive_Bayes'
update(object, smoothness = NULL, Laplace = NULL, fresh = FALSE, ...)
## S3 method for class 'nearest_neighbor'
update(
object,
parameters = NULL,
neighbors = NULL,
weight_func = NULL,
dist_power = NULL,
fresh = FALSE,
...
)
## S3 method for class 'pls'
update(
object,
parameters = NULL,
predictor_prop = NULL,
num_comp = NULL,
fresh = FALSE,
...
)
## S3 method for class 'poisson_reg'
update(
object,
parameters = NULL,
penalty = NULL,
mixture = NULL,
fresh = FALSE,
...
)
## S3 method for class 'proportional_hazards'
update(
object,
parameters = NULL,
penalty = NULL,
mixture = NULL,
fresh = FALSE,
...
)
## S3 method for class 'rand_forest'
update(
object,
parameters = NULL,
mtry = NULL,
trees = NULL,
min_n = NULL,
fresh = FALSE,
...
)
## S3 method for class 'rule_fit'
update(
object,
parameters = NULL,
mtry = NULL,
trees = NULL,
min_n = NULL,
tree_depth = NULL,
learn_rate = NULL,
loss_reduction = NULL,
sample_size = NULL,
penalty = NULL,
fresh = FALSE,
...
)
## S3 method for class 'surv_reg'
update(object, parameters = NULL, dist = NULL, fresh = FALSE, ...)
## S3 method for class 'survival_reg'
update(object, parameters = NULL, dist = NULL, fresh = FALSE, ...)
## S3 method for class 'svm_linear'
update(
object,
parameters = NULL,
cost = NULL,
margin = NULL,
fresh = FALSE,
...
)
## S3 method for class 'svm_poly'
update(
object,
parameters = NULL,
cost = NULL,
degree = NULL,
scale_factor = NULL,
margin = NULL,
fresh = FALSE,
...
)
## S3 method for class 'svm_rbf'
update(
object,
parameters = NULL,
cost = NULL,
rbf_sigma = NULL,
margin = NULL,
fresh = FALSE,
...
)
object 
A model specification. 
parameters 
A 1row tibble or named list with main
parameters to update. Use either 
num_terms 
The number of features that will be retained in the final model, including the intercept. 
prod_degree 
The highest possible interaction degree. 
prune_method 
The pruning method. 
fresh 
A logical for whether the arguments should be modified inplace or replaced wholesale. 
... 
Not used for 
An integer for the number of units in the hidden model.  
penalty 
An nonnegative number representing the amount of regularization used by some of the engines. 
epochs 
An integer for the number of training iterations. 
cost_complexity 
A positive number for the the cost/complexity
parameter (a.k.a. 
tree_depth 
An integer for maximum depth of the tree. 
min_n 
An integer for the minimum number of data points in a node that are required for the node to be split further. 
class_cost 
A nonnegative scalar for a class cost (where a cost of 1 means no extra cost). This is useful for when the first level of the outcome factor is the minority class. If this is not the case, values between zero and one can be used to bias to the second level of the factor. 
trees 
An integer for the number of trees contained in the ensemble. 
prior_terminal_node_coef 
A coefficient for the prior probability that a node is a terminal node. 
prior_terminal_node_expo 
An exponent in the prior probability that a node is a terminal node. 
prior_outcome_range 
A positive value that defines the width of a prior that the predicted outcome is within a certain range. For regression it is related to the observed range of the data; the prior is the number of standard deviations of a Gaussian distribution defined by the observed range of the data. For classification, it is defined as the range of +/3 (assumed to be on the logit scale). The default value is 2. 
mtry 
A number for the number (or proportion) of predictors that will be randomly sampled at each split when creating the tree models (specific engines only). 
learn_rate 
A number for the rate at which the boosting algorithm adapts from iterationtoiteration (specific engines only). This is sometimes referred to as the shrinkage parameter. 
loss_reduction 
A number for the reduction in the loss function required to split further (specific engines only). 
sample_size 
A number for the number (or proportion) of data that is
exposed to the fitting routine. For 
stop_iter 
The number of iterations without improvement before stopping (specific engines only). 
committees 
A nonnegative integer (no greater than 100) for the number of members of the ensemble. 
neighbors 
An integer between zero and nine for the number of training set instances that are used to adjust the modelbased prediction. 
max_rules 
The largest number of rules. 
regularization_method 
A character string for the type of regularized
estimation. Possible values are: " 
frac_common_cov , frac_identity 
Numeric values between zero and one. 
select_features 

adjust_deg_free 
If 
mixture 
A number between zero and one (inclusive) denoting the proportion of L1 regularization (i.e. lasso) in the model.
Available for specific engines only. 
dropout 
A number between 0 (inclusive) and 1 denoting the proportion of model parameters randomly set to zero during model training. 
activation 
A single character string denoting the type of relationship between the original predictors and the hidden unit layer. The activation function between the hidden and output layers is automatically set to either "linear" or "softmax" depending on the type of outcome. Possible values depend on the engine being used. 
smoothness 
An nonnegative number representing the the relative smoothness of the class boundary. Smaller examples result in model flexible boundaries and larger values generate class boundaries that are less adaptable 
Laplace 
A nonnegative value for the Laplace correction to smoothing lowfrequency counts. 
weight_func 
A single character for the type of kernel function used
to weight distances between samples. Valid choices are: 
dist_power 
A single number for the parameter used in calculating Minkowski distance. 
predictor_prop 
The maximum proportion of original predictors that can have nonzero coefficients for each PLS component (via regularization). This value is used for all PLS components for X. 
num_comp 
The number of PLS components to retain. 
dist 
A character string for the probability distribution of the outcome. The default is "weibull". 
cost 
A positive number for the cost of predicting a sample within or on the wrong side of the margin 
margin 
A positive number for the epsilon in the SVM insensitive loss function (regression only) 
degree 
A positive number for polynomial degree. 
scale_factor 
A positive number for the polynomial scaling factor. 
rbf_sigma 
A positive number for radial basis function. 
An updated model specification.
# 
model < C5_rules(trees = 10, min_n = 2)
model
update(model, trees = 1)
update(model, trees = 1, fresh = TRUE)
# 
model < cubist_rules(committees = 10, neighbors = 2)
model
update(model, committees = 1)
update(model, committees = 1, fresh = TRUE)
model < pls(predictor_prop = 0.1)
model
update(model, predictor_prop = 1)
update(model, predictor_prop = 1, fresh = TRUE)
# 
model < rule_fit(trees = 10, min_n = 2)
model
update(model, trees = 1)
update(model, trees = 1, fresh = TRUE)
model < boost_tree(mtry = 10, min_n = 3)
model
update(model, mtry = 1)
update(model, mtry = 1, fresh = TRUE)
param_values < tibble::tibble(mtry = 10, tree_depth = 5)
model %>% update(param_values)
model %>% update(param_values, mtry = 3)
param_values$verbose < 0
# Fails due to engine argument
# model %>% update(param_values)
model < linear_reg(penalty = 10, mixture = 0.1)
model
update(model, penalty = 1)
update(model, penalty = 1, fresh = TRUE)
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