org.opencv.ml
public class CvGBTreesParams extends CvDTreeParams
GBT training parameters.
The structure contains parameters for each single decision tree in the ensemble, as well as the whole model characteristics. The structure is derived from "CvDTreeParams" but not all of the decision tree parameters are supported: cross-validation, pruning, and class priorities are not used.
Constructor and Description |
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CvGBTreesParams()
By default the following constructor is used: CvGBTreesParams(CvGBTrees.SQUARED_LOSS,
200, 0.8f, 0.01f, 3, false)
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Modifier and Type | Method and Description |
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int |
get_loss_function_type() |
float |
get_shrinkage() |
float |
get_subsample_portion() |
int |
get_weak_count() |
void |
set_loss_function_type(int loss_function_type) |
void |
set_shrinkage(float shrinkage) |
void |
set_subsample_portion(float subsample_portion) |
void |
set_weak_count(int weak_count) |
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 CvGBTreesParams()
By default the following constructor is used: CvGBTreesParams(CvGBTrees.SQUARED_LOSS,
200, 0.8f, 0.01f, 3, false)
// C++ code:
: CvDTreeParams(3, 10, 0, false, 10, 0, false, false, 0)
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Method Detail
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get_loss_function_type
public int get_loss_function_type()
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get_shrinkage
public float get_shrinkage()
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get_subsample_portion
public float get_subsample_portion()
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get_weak_count
public int get_weak_count()
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set_loss_function_type
public void set_loss_function_type(int loss_function_type)
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set_shrinkage
public void set_shrinkage(float shrinkage)
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set_subsample_portion
public void set_subsample_portion(float subsample_portion)
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set_weak_count
public void set_weak_count(int weak_count)