org.opencv.ml
public class CvRTParams extends CvDTreeParams
Training parameters of random trees.
The set of training parameters for the forest is a superset of the training parameters for a single tree. However, random trees do not need all the functionality/features of decision trees. Most noticeably, the trees are not pruned, so the cross-validation parameters are not used.
Constructor and Description |
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CvRTParams() |
Modifier and Type | Method and Description |
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boolean |
get_calc_var_importance() |
int |
get_nactive_vars() |
TermCriteria |
get_term_crit() |
void |
set_calc_var_importance(boolean calc_var_importance) |
void |
set_nactive_vars(int nactive_vars) |
void |
set_term_crit(TermCriteria term_crit) |
get_cv_folds, get_max_categories, get_max_depth, get_min_sample_count, get_regression_accuracy, get_truncate_pruned_tree, get_use_1se_rule, get_use_surrogates, set_cv_folds, set_max_categories, set_max_depth, set_min_sample_count, set_regression_accuracy, set_truncate_pruned_tree, set_use_1se_rule, set_use_surrogates
public boolean get_calc_var_importance()
public int get_nactive_vars()
public TermCriteria get_term_crit()
public void set_calc_var_importance(boolean calc_var_importance)
public void set_nactive_vars(int nactive_vars)
public void set_term_crit(TermCriteria term_crit)