Parameter docs shared by gpb.train, gpb.cv, and gpboost
List of callback functions that are applied at each iteration.
a gpb.Dataset object, used for training. Some functions, such as gpb.cv,
may allow you to pass other types of data like matrix and then separately supply
label as a keyword argument.
int. Activates early stopping. Requires at least one validation data
and one metric. When this parameter is non-null,
training will stop if the evaluation of any metric on any validation set
fails to improve for early_stopping_rounds consecutive boosting rounds.
If training stops early, the returned model will have attribute best_iter
set to the iteration number of the best iteration.
evaluation function(s). This can be a character vector, function, or list with a mixture of strings and functions.
a. character vector: If you provide a character vector to this argument, it should contain strings with valid evaluation metrics. See the "metric" section of the parameter documentation for a list of valid metrics.
b. function:
You can provide a custom evaluation function. This
should accept the keyword arguments preds and dtrain and should return a named
list with three elements:
name: A string with the name of the metric, used for printing
and storing results.
value: A single number indicating the value of the metric for the
given predictions and true values
higher_better: A boolean indicating whether higher values indicate a better fit.
For example, this would be FALSE for metrics like MAE or RMSE.
c. list: If a list is given, it should only contain character vectors and functions. These should follow the requirements from the descriptions above.
evaluation output frequency, only effect when verbose > 0
a list of gpb.Dataset objects, used for validation
Boolean, TRUE will record iteration message to booster$record_evals
feature names, if not null, will use this to overwrite the names in dataset
categorical features. This can either be a character vector of feature
names or an integer vector with the indices of the features (e.g.
c(1L, 10L) to say "the first and tenth columns").
path of model file of gpb.Booster object, will continue training from this model
number of boosting iterations (= number of trees). This is the most important tuning parameter for boosting. Default = 100
objective function, can be character or custom objective function. Examples include
regression, regression_l1, huber,
binary, lambdarank, multiclass, multiclass
list of ("tuning") parameters. See the parameter documentation for more information. A few key parameters:
learning_rate The learning rate, also called shrinkage or damping parameter
(default = 0.1). An important tuning parameter for boosting. Lower values usually
lead to higher predictive accuracy but more boosting iterations are needed
num_leaves Number of leaves in a tree. Tuning parameter for
tree-boosting (default = 31)
min_data_in_leaf Minimal number of samples per leaf. Tuning parameter for
tree-boosting (default = 20)
max_depth Maximal depth of a tree. Tuning parameter for tree-boosting (default = no limit)
leaves_newton_update Set this to TRUE to do a Newton update step for the tree leaves
after the gradient step. Applies only to Gaussian process boosting (GPBoost algorithm)
train_gp_model_cov_pars If TRUE, the covariance parameters of the Gaussian process
are stimated in every boosting iterations,
otherwise the gp_model parameters are not estimated. In the latter case, you need to
either esimate them beforehand or provide the values via
the 'init_cov_pars' parameter when creating the gp_model (default = TRUE).
use_gp_model_for_validation If TRUE, the Gaussian process is also used
(in addition to the tree model) for calculating predictions on the validation data
(default = TRUE)
use_nesterov_acc Set this to TRUE to do boosting with Nesterov acceleration (default = FALSE).
Can currently only be used for tree_learner = "serial" (default option)
nesterov_acc_rate Acceleration rate for momentum step in case Nesterov accelerated
boosting is used (default = 0.5)
oosting Boosting type. "gbdt", "rf", "dart" or "goss".
Only "gbdt" allows for doing Gaussian process boosting.
num_threads Number of threads. For the best speed, set this to
the number of real CPU cores(parallel::detectCores(logical = FALSE)),
not the number of threads (most CPU using hyper-threading to generate 2 threads
per CPU core).
verbosity for output, if <= 0, also will disable the print of evaluation during training
A GPModel object that contains the random effects (Gaussian process and / or grouped random effects) model
Boolean (default = TRUE). If TRUE, the gp_model
(Gaussian process and/or random effects) is also used (in addition to the tree model) for calculating
predictions on the validation data. If FALSE, the gp_model (random effects part) is ignored
for making predictions and only the tree ensemble is used for making predictions for calculating the validation / test error.
Boolean (default = TRUE). If TRUE, the covariance parameters
of the gp_model (Gaussian process and/or random effects) are estimated in every
boosting iterations, otherwise the gp_model parameters are not estimated.
In the latter case, you need to either estimate them beforehand or provide the values via
the init_cov_pars parameter when creating the gp_model
"early stopping" refers to stopping the training process if the model's performance on a given validation set does not improve for several consecutive iterations.
If multiple arguments are given to eval, their order will be preserved. If you enable
early stopping by setting early_stopping_rounds in params, by default all
metrics will be considered for early stopping.
If you want to only consider the first metric for early stopping, pass
first_metric_only = TRUE in params. Note that if you also specify metric
in params, that metric will be considered the "first" one. If you omit metric,
a default metric will be used based on your choice for the parameter obj (keyword argument)
or objective (passed into params).