.. ocv:function:: CvRTParams::CvRTParams( int max_depth, int min_sample_count, float regression_accuracy, bool use_surrogates, int max_categories, const float* priors, bool calc_var_importance, int nactive_vars, int max_num_of_trees_in_the_forest, float forest_accuracy, int termcrit_type )
:param max_depth: the depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods.
:param min_sample_count: minimum samples required at a leaf node for it to be split. A reasonable value is a small percentage of the total data e.g. 1%.
:param max_categories: Cluster possible values of a categorical variable into ``K`` :math:`\leq` ``max_categories`` clusters to find a suboptimal split. If a discrete variable, on which the training procedure tries to make a split, takes more than ``max_categories`` values, the precise best subset estimation may take a very long time because the algorithm is exponential. Instead, many decision trees engines (including ML) try to find sub-optimal split in this case by clustering all the samples into ``max_categories`` clusters that is some categories are merged together. The clustering is applied only in ``n``>2-class classification problems for categorical variables with ``N > max_categories`` possible values. In case of regression and 2-class classification the optimal split can be found efficiently without employing clustering, thus the parameter is not used in these cases.
:param calc_var_importance: If true then variable importance will be calculated and then it can be retrieved by :ocv:func:`CvRTrees::get_var_importance`.
:param nactive_vars: The size of the randomly selected subset of features at each tree node and that are used to find the best split(s). If you set it to 0 then the size will be set to the square root of the total number of features.
:param max_num_of_trees_in_the_forest: The maximum number of trees in the forest (suprise, suprise).
:param max_num_of_trees_in_the_forest: The maximum number of trees in the forest (suprise, suprise). Typically the more trees you have the better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain number of trees. Also to keep in mind, the number of tree increases the prediction time linearly.
