INNER CODE UNIT · Python
grid_search_tune_parameters
fabsig/GPBoost · python-package/gpboost/engine.py:921
def grid_search_tune_parameters(param_grid, train_set, gp_model=None, num_try_random=None, params=None,
num_boost_round=1000, early_stopping_rounds=None,
folds=None, nfold=5, metric=None,
use_gp_model_for_validation=True, train_gp_model_cov_pars=True,
stratified=False, shuffle=True, fobj=None, feval=None, init_model=None,
feature_name='auto', categorical_feature='auto', fpreproc=None,
verbose_eval=1, seed=0, callbacks=None, metrics=None,
return_all_combinations=False):
"""Function for choosing tuning parameters from a grid in a determinstic or random way using cross validation or validation data sets.
Parameters
----------
param_grid : dict
Candidate parameters defining the grid over which a search is done.
See https://github.com/fabsig/GPBoost/blob/master/docs/Main_parameters.rst#tuning-parameters--hyperparameters-for-the-tree-boosting-part
train_set : Dataset
Data to be trained on.
gp_model : GPModel or None, optional (default=None)