Batch Processing

Decision tree regression follows the general workflow described in Training and Prediction > Regression > Usage Model.

Training

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

At the training stage, decision tree regression 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 decision tree regression. The only training method supported so far is the default dense method.

pruning

reducedErrorPruning

Method to perform post-pruning. Available options for the pruning parameter:

  • reducedErrorPruning - reduced error pruning. Provide dataForPruning and dependentVariablesForPruning inputs, if you use pruning.
  • none - do not prune.

maxTreeDepth

0

Maximum tree depth. Zero value means unlimited depth. Can be any non-negative number.

minObservationsInLeafNodes

5

Minimum number of observations in the leaf node. Can be any positive number.

pruningFraction

0.2

Fraction of observations from training dataset to be used as observations for post-pruning via random sampling. The rest observations (with fraction 1- pruningFraction to be used to build a decision tree). Can be any number in the interval (0, 1). If pruning is not used, all observations are used to build the decision tree regardless of this parameter value.

engine

SharedPtr<engines::mt19937::Batch<> >()

Pointer to the random number engine to be used for random sampling for reduced error post-pruning.

Prediction

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

At the prediction stage, decision tree regression 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 decision tree regression. The only training method supported so far is the default dense method.

For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)