Batch Processing

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

Training

For the description of the input and output, refer to Training and Prediction > Classification > Usage Model. In addition to common input, decision trees can accept the following inputs used for post-pruning:

Input ID

Input

dataForPruning

Pointer to the m x p numeric table with the pruning data set. This table can be an object of any class derived from NumericTable.

labelsForPruning

Pointer to the m x 1 numeric table with class labels. This table can be an object of any class derived from NumericTable except PackedSymmetricMatrix and PackedTriangularMatrix.

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

nClasses

Not applicable

The number of classes. A required parameter.

splitCriterion

infoGain

Split criterion to choose the best test for split nodes. Available split criteria for decision trees:

  • gini - the Gini index
  • infoGain - the information gain

pruning

reducedErrorPruning

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

  • reducedErrorPruning - reduced error pruning. Provide dataForPruning and labelsForPruning 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

1

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

Prediction

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

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

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