Batch Processing

AdaBoost classifier follows the general workflow described in Usage Model: Training and Prediction.

Training

For a description of the input and output, refer to Usage Model: Training and Prediction.

At the training stage, an AdaBoost classifier has the following parameters:

Parameter	Default Value	Description
algorithmFPType	double	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
method	defaultDense	The computation method used by the AdaBoost classifier. The only training method supported so far is the Y. Freund's method.
weakLearnerTraining	Pointer to an object of the stump training class	Pointer to the training algorithm of the weak learner. By default, a stump weak learner is used.
weakLearnerPrediction	Pointer to an object of the stump prediction class	Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.
accuracyThreshold	0.01	AdaBoost training accuracy.
maxIterations	100	The maximal number of iterations for the algorithm.

For a description of the input and output, refer to Usage Model: Training and Prediction.

At the prediction stage, an AdaBoost classifier has the following parameters:

Parameter	Default Value	Description
algorithmFPType	double	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
method	defaultDense	Performance-oriented computation method, the only method supported by the AdaBoost classifier at the prediction stage.
weakLearnerPrediction	Pointer to an object of the stump prediction class	Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.