Boosting is a powerful learning concept that provides a solution to the supervised classification learning task. It combines the performance of many "weak" classifiers to produce a powerful committee [HTF01]_. A weak classifier is only required to be better than chance, and thus can be very simple and computationally inexpensive. However, many of them smartly combine results to a strong classifier that often outperforms most "monolithic" strong classifiers such as SVMs and Neural Networks.
Decision trees are the most popular weak classifiers used in boosting schemes. Often the simplest decision trees with only a single split node per tree (called ``stumps`` ) are sufficient.
:math:`K` -component vector. Each component encodes a feature relevant to the learning task at hand. The desired two-class output is encoded as -1 and +1.
Different variants of boosting are known as Discrete Adaboost, Real AdaBoost, LogitBoost, and Gentle AdaBoost [FHT98]_. All of them are very similar in their overall structure. Therefore, this chapter focuses only on the standard two-class Discrete AdaBoost algorithm, outlined below. Initially the same weight is assigned to each sample (step 2). Then, a weak classifier
:math:`c_m` is computed (step 3b). The weights are increased for training samples that have been misclassified (step 3c). All weights are then normalized, and the process of finding the next weak classifier continues for another
..note:: Similar to the classical boosting methods, the current implementation supports two-class classifiers only. For ``M > 2`` classes, there is the **AdaBoost.MH** algorithm (described in [FHT98]_) that reduces the problem to the two-class problem, yet with a much larger training set.
To reduce computation time for boosted models without substantially losing accuracy, the influence trimming technique can be employed. As the training algorithm proceeds and the number of trees in the ensemble is increased, a larger number of the training samples are classified correctly and with increasing confidence, thereby those samples receive smaller weights on the subsequent iterations. Examples with a very low relative weight have a small impact on the weak classifier training. Thus, such examples may be excluded during the weak classifier training without having much effect on the induced classifier. This process is controlled with the ``weight_trim_rate`` parameter. Only examples with the summary fraction ``weight_trim_rate`` of the total weight mass are used in the weak classifier training. Note that the weights for
training examples are recomputed at each training iteration. Examples deleted at a particular iteration may be used again for learning some of the weak classifiers further [FHT98]_.
..[HTF01] Hastie, T., Tibshirani, R., Friedman, J. H. *The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics*. 2001.
..[FHT98] Friedman, J. H., Hastie, T. and Tibshirani, R. Additive Logistic Regression: a Statistical View of Boosting. Technical Report, Dept. of Statistics*, Stanford University, 1998.
The structure is derived from :ocv:class:`CvDTreeParams` but not all of the decision tree parameters are supported. In particular, cross-validation is not supported.
: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.
The weak tree classifier, a component of the boosted tree classifier :ocv:class:`CvBoost`, is a derivative of :ocv:class:`CvDTree`. Normally, there is no need to use the weak classifiers directly. However, they can be accessed as elements of the sequence ``CvBoost::weak``, retrieved by :ocv:func:`CvBoost::get_weak_predictors`.
..note:: In case of LogitBoost and Gentle AdaBoost, each weak predictor is a regression tree, rather than a classification tree. Even in case of Discrete AdaBoost and Real AdaBoost, the ``CvBoostTree::predict`` return value (:ocv:member:`CvDTreeNode::value`) is not an output class label. A negative value "votes" for class #0, a positive value - for class #1. The votes are weighted. The weight of each individual tree may be increased or decreased using the method ``CvBoostTree::scale``.
:param update:Specifies whether the classifier needs to be updated (``true``, the new weak tree classifiers added to the existing ensemble) or the classifier needs to be rebuilt from scratch (``false``).
The train method follows the common template of :ocv:func:`CvStatModel::train`. The responses must be categorical, which means that boosted trees cannot be built for regression, and there should be two classes.
:param missing:Optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits (see ``CvDTreeParams::use_surrogates``).
:param weak_responses:Optional output parameter, a floating-point vector with responses of each individual weak classifier. The number of elements in the vector must be equal to the slice length.
The method returns the sequence of weak classifiers. Each element of the sequence is a pointer to the :ocv:class:`CvBoostTree` class or to some of its derivatives.
CvBoost::get_params
-------------------
Returns current parameters of the boosted tree classifier.
