gpb_shared_params: Shared parameter docs
In gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

gpb_shared_params

R Documentation

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.
`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.
`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).