Boosting is a set of algorithms intended to build a strong classifier from an ensemble of weighted weak classifiers by iterative re-weighting according to some accuracy measure for weak classifiers. A weak learner is a classifier that has only slightly better performance than random guessing. Weak learners are usually very simple and fast, and they focus on classification of very specific features.

Boosting algorithms include LogitBoost, BrownBoost, AdaBoost, and others. A Decision Stump classifier is one of the popular weak learners.

In Intel DAAL, a Weak Learner is a set of interface classes that define methods to enable use of a weak learner classifier. The Weak Learner classes include Training, Prediction, and Model. Specific weak learner classifiers, such as stump, implement those methods.

Intel DAAL boosting algorithms pass pointers to weak learner training and prediction objects through the parameters of boosting algorithms. Use the getNumberOfWeakLearners() method to determine the number of weak learners trained.

You can implement your own weak learners by deriving from the appropriate interface classes.

Note

When defining your own weak learners to use with boosting classifiers, make sure the prediction component of your weak learner returns the number from the interval [-1.0, 1.0].

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