Batch Processing

Decision forest classification follows the general workflow described in Training and Prediction > Classification > Usage Model and Training and Prediction > Classification and Regression > Decision Forest.

Training

In addition to the parameters of classifier and decision forest described in Training and Prediction > Classification > Usage Model and Classification and Regression > Decision Forest > Batch Processing, the decision forest classification training algorithm has the following parameters:

Parameter

Default Value

Description

algorithmFPType

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

method

defaultDense

The computation method used by the decision forest classification. The only prediction method supported so far is the default dense method.

nClasses

Not applicable.

The number of classes. A required parameter.

Output

Decision forest classification calculates the result of regression and decision forest. For more details, refer to Training and Prediction > Classification > Usage Model, Classification and Regression > Decision Forest > Batch Processing.

Prediction

For the description of the input and output, refer to Training and Prediction > Classification > Usage Model.

In addition to the parameters of the classifier, decision forest classification has the following parameters at the prediction stage:

Parameter

Default Value

Description

algorithmFPType

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

method

defaultDense

The computation method used by the decision forest classification. The only prediction method supported so far is the default dense method.

nClasses

Not applicable.

The number of classes. A required parameter.

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