Batch Processing

LogitBoost 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, a LogitBoost classifier has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

The computation method used by the LogitBoost classifier. The only training method supported so far is the Friedman 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

LogitBoost training accuracy.

maxIterations

100

The maximal number of iterations for the LogitBoost algorithm.

nClasses

Not applicable

The number of classes, a required parameter.

weightsDegenerateCasesThreshold

1e-10

The threshold to avoid degenerate cases when calculating weights wij.

responsesDegenerateCasesThreshold

1e-10

The threshold to avoid degenerate cases when calculating responses zij.

Prediction

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

At the prediction stage, a LogitBoost classifier has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

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 LogitBoost 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.

nClasses

Not applicable

The number of classes, a required parameter.

Note

The algorithm terminates if it achieves the specified accuracy or reaches the specified maximal number of iterations. To determine the actual number of iterations performed, call the getNumberOfWeakLearners() method of the LogitBoostModel class and divide it by nClasses.

For more complete information about compiler optimizations, see our Optimization Notice.
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