gpb_shared_params: Shared parameter docs

In gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

Shared parameter docs

Description

Parameter docs shared by gpb.train, gpb.cv, and gpboost

Arguments

callbacks	List of callback functions that are applied at each iteration.
data	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.
folds	list provides a possibility to use a list of pre-defined CV folds (each element must be a vector of test fold's indices). When folds are supplied, the nfold and stratified parameters are ignored.
nfold	the original dataset is randomly partitioned into nfold equal size subsamples.
cv_seed	Seed for generating folds when doing nfold CV
early_stopping_rounds	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.
metric	Evaluation metric to be monitored when doing CV and parameter tuning. Can be a character string or vector of character strings. If not NULL, the metric in params will be overridden. Non-exhaustive list of supported metrics: "test_neg_log_likelihood", "mse", "rmse", "mae", "auc", "average_precision", "binary_logloss", "binary_error". See the "metric" section of the parameter documentation for a complete list of valid metrics.
verbose_eval	integer. Whether to display information on the progress of tuning parameter choice. If None or 0, verbose is of. If = 1, summary progress information is displayed for every parameter combination. If >= 2, detailed progress is displayed at every boosting stage for every parameter combination.
eval	Evaluation metric to be monitored when doing CV and parameter tuning. This can be a string, function, or list with a mixture of strings and functions. a. character vector: Non-exhaustive list of supported metrics: "test_neg_log_likelihood", "mse", "rmse", "mae", "auc", "average_precision", "binary_logloss", "binary_error" See the "metric" section of the parameter documentation for a complete 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.
eval_freq	evaluation output frequency, only effect when verbose > 0
valids	a list of gpb.Dataset objects, used for validation
record	Boolean, TRUE will record iteration message to booster$record_evals
colnames	feature names, if not null, will use this to overwrite the names in dataset
categorical_feature	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").
init_model	path of model file of gpb.Booster object, will continue training from this model
nrounds	number of boosting iterations (= number of trees). This is the most important tuning parameter for boosting
obj	(character) The distribution of the response variable (=label) conditional on fixed and random effects. This only needs to be set when doing independent boosting without random effects / Gaussian processes.
params	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) max_depth: Maximal depth of a tree. Tuning parameter for tree-boosting (default = no limit) min_data_in_leaf: Minimal number of samples per leaf. Tuning parameter for tree-boosting (default = 20) lambda_l2: L2 regularization (default = 0) lambda_l1: L1 regularization (default = 0) max_bin: Maximal number of bins that feature values will be bucketed in (default = 255) line_search_step_length (default = FALSE): If TRUE, a line search is done to find the optimal step length for every boosting update (see, e.g., Friedman 2001). This is then multiplied by the learning rate train_gp_model_cov_pars (default = TRUE): If TRUE, the covariance parameters of the Gaussian process 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 values via the 'init_cov_pars' parameter when creating the gp_model use_gp_model_for_validation (default = TRUE): If TRUE, the Gaussian process is also used (in addition to the tree model) for calculating predictions on the validation data leaves_newton_update (default = FALSE): 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) 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).
verbose	verbosity for output, if <= 0, also will disable the print of evaluation during training
gp_model	A GPModel object that contains the random effects (Gaussian process and / or grouped random effects) model
line_search_step_length	Boolean. If TRUE, a line search is done to find the optimal step length for every boosting update (see, e.g., Friedman 2001). This is then multiplied by the learning_rate. Applies only to the GPBoost algorithm
use_gp_model_for_validation	Boolean. 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.
train_gp_model_cov_pars	Boolean. 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

"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).

gpboost documentation built on April 3, 2025, 10:58 p.m.