CvBoostParams( int boost_type, int weak_count, double weight_trim_rate,
int max_depth, bool use_surrogates, const float* priors );
};
The structure is derived from :ref:`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.
.. index:: CvBoostParams::CvBoostParams
.. _CvBoostParams::CvBoostParams:
CvBoostParams::CvBoostParams
----------------------------
.. ocv:function:: CvBoostParams::CvBoostParams()
.. ocv:function:: CvBoostParams::CvBoostParams( int boost_type, int weak_count, double weight_trim_rate, int max_depth, bool use_surrogates, const float* priors )
:param boost_type: Type of the boosting algorithm. Possible values are:
* **CvBoost::DISCRETE** Discrete AbaBoost.
* **CvBoost::REAL** Real AdaBoost. It is a technique that utilizes confidence-rated predictions and works well with categorical data.
* **CvBoost::LOGIT** LogitBoost. It can produce good regression fits.
* **CvBoost::GENTLE** Gentle AdaBoost. It puts less weight on outlier data points and for that reason is often good with regression data.
The structure is derived from
:ref:`CvDTreeParams` but not all of the decision tree parameters are supported. In particular, cross-validation is not supported.
Often the "real" and "gentle" forms of AdaBoost work best.
:param weak_count: The number of weak classifiers.
:param weight_trim_rate: A threshold between 0 and 1 used to save computational time. Samples with summary weight :math:`\leq 1 - weight\_trim\_rate` do not participate in the *next* iteration of training. Set this parameter to 0 to turn off this functionality.
See :ref:`CvDTreeParams::CvDTreeParams` for description of other parameters.
Also there is one parameter that you can set directly.
:param split_criteria: Splitting criteria used to choose optimal splits during a weak tree construction. Possible values are:
* **CvBoost::DEFAULT** Use the default for the particular boosting method.
* **CvBoost::GINI** Default option for real AdaBoost.
* **CvBoost::MISCLASS** Default option for discrete AdaBoost.
* **CvBoost::SQERR** Least-square error; only option available for LogitBoost and gentle AdaBoost.
.. index:: CvBoostTree
...
...
@@ -199,6 +220,24 @@ The method removes the specified weak classifiers from the sequence.
Do not confuse this method with the pruning of individual decision trees, which is currently not supported.
.. index:: CvBoost::get_weak_predictors
.. _CvBoost::get_weak_predictors:
.. index:: CvBoost::calc_error
.. _CvBoost::calc_error:
CvBoost::calc_error
-------------------
.. ocv:function:: float CvBoost::calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 )
Returns error of the boosted tree classifier.
The method is identical to :ocv:func:`CvDTree::calc_error` but uses the boosted tree classifier as predictor.
.. index:: CvBoost::get_weak_predictors
.. _CvBoost::get_weak_predictors:
...
...
@@ -211,3 +250,26 @@ CvBoost::get_weak_predictors
The method returns the sequence of weak classifiers. Each element of the sequence is a pointer to the ``CvBoostTree`` class or, probably, to some of its derivatives.
