org.opencv.ml
public class CvBoostParams extends CvDTreeParams
Boosting training parameters.
There is one structure member that you can set directly:
Splitting criteria used to choose optimal splits during a weak tree construction. Possible values are:
// C++ code:
The structure is derived from "CvDTreeParams" but not all of the decision tree parameters are supported. In particular, cross-validation is not supported.
All parameters are public. You can initialize them by a constructor and then override some of them directly if you want.
Constructor and Description |
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CvBoostParams()
The constructors.
|
Modifier and Type | Method and Description |
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int |
get_boost_type() |
int |
get_split_criteria() |
int |
get_weak_count() |
double |
get_weight_trim_rate() |
void |
set_boost_type(int boost_type) |
void |
set_split_criteria(int split_criteria) |
void |
set_weak_count(int weak_count) |
void |
set_weight_trim_rate(double weight_trim_rate) |
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 CvBoostParams()
The constructors.
See "CvDTreeParams.CvDTreeParams" for description of other parameters.
Default parameters are:
// C++ code:
CvBoostParams.CvBoostParams()
boost_type = CvBoost.REAL;
weak_count = 100;
weight_trim_rate = 0.95;
cv_folds = 0;
max_depth = 1;
public int get_boost_type()
public int get_split_criteria()
public int get_weak_count()
public double get_weight_trim_rate()
public void set_boost_type(int boost_type)
public void set_split_criteria(int split_criteria)
public void set_weak_count(int weak_count)
public void set_weight_trim_rate(double weight_trim_rate)