adding doc

deephyper/search/hps/_cbo.py

@@ -87,6 +87,7 @@ class CBO(Search):
         log_dir (str, optional): Log directory where search's results are saved. Defaults to ``"."``.
         verbose (int, optional): Indicate the verbosity level of the search. Defaults to ``0``.
         surrogate_model (Union[str,sklearn.base.RegressorMixin], optional): Surrogate model used by the Bayesian optimization. Can be a value in ``["RF", "GP", "ET", "MF", "GBRT", "DUMMY"]`` or a sklearn regressor. ``"ET"`` is for Extremely Randomized Trees which is the best compromise between speed and quality when performing a lot of parallel evaluations, i.e., reaching more than hundreds of evaluations. ``"GP"`` is for Gaussian-Process which is the best choice when maximizing the quality of iteration but quickly slow down when reaching hundreds of evaluations, also it does not support conditional search space. ``"RF"`` is for Random-Forest, slower than extremely randomized trees but with better mean estimate and worse epistemic uncertainty quantification capabilities. ``"GBRT"`` is for Gradient-Boosting Regression Tree, it has better mean estimate than other tree-based method worse uncertainty quantification capabilities and slower than ``"RF"``. Defaults to ``"ET"``.
+        surrogate_model_kwargs (dict, optional): Additional parameters to pass to the surrogate model. Defaults to ``None``.
         acq_func (str, optional): Acquisition function used by the Bayesian optimization. Can be a value in ``["UCB", "EI", "PI", "gp_hedge"]``. Defaults to ``"UCB"``.
         acq_optimizer (str, optional): Method used to minimze the acquisition function. Can be a value in ``["sampling", "lbfgs", "ga"]``. Defaults to ``"auto"``.
         acq_optimizer_freq (int, optional): Frequency of optimization calls for the acquisition function. Defaults to ``10``, using optimizer every ``10`` surrogate model updates.