Developer Guide for Intel® Data Analytics Acceleration Library 2018
Decision tree regression follows the general workflow described in Training and Prediction > Regression > Usage Model.
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:
|
|
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. |
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. |