Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
Decision tree classification follows the general workflow described in Training and Prediction > Classification > Usage Model.
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. |
|
splitCriterion |
infoGain |
Split criterion to choose the best test for split nodes. Available split criteria for decision trees:
|
|
pruning |
reducedErrorPruning |
Method to perform post-pruning. Available options for the pruning parameter:
|
|
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. |
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. |