Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

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Parameter Class Reference

Base class for parameters of the decision forest regression training algorithm. More...

Detailed Description

Member Function Documentation

long getFeaturesPerNode ( )

Returns number of features tried as possible splits by the decision forest regression training algorithm If 0 then p/3 is used, where p is the total number of features.

Returns
Number of features
double getImpurityThreshold ( )

Returns threshold value used as stopping criteria: if the impurity value in the node is smaller than the threshold then the node is not split anymore

Returns
Impurity threshold
long getMaxTreeDepth ( )

Returns maximal tree depth. Default is 0 (unlimited)

Returns
Maximal tree depth
long getMinObservationsInLeafNode ( )

Returns minimal number of samples per node. Default is 5

Returns
Minimal number of samples
long getNTrees ( )

Returns number of trees to be created by the decision forest regression training algorithm

Returns
Number of trees
double getObservationsPerTreeFraction ( )

Returns fraction of observations used for a training of one tree, 0 to 1. Default is 1 (sampling with replacement)

Returns
Fraction of observations
long getResultsToCompute ( )

Gets the 64 bit integer flag that indicates the results to compute

Returns
The 64 bit integer flag that indicates the results to compute
int getSeed ( )
Deprecated:
This item will be removed in a future release.

Returns the seed for the random numbers generator used by the algorithm

Returns
Seed for the seed for the random numbers generator used by the algorithm
VariableImportanceModeId getVariableImportanceMode ( )

Returns variable importance computation mode

Returns
Variable importance computation mode
void setEngine ( com.intel.daal.algorithms.engines.BatchBase  engine)

Sets the engine to be used by the algorithm

Parameters
engineto be used by the algorithm
void setFeaturesPerNode ( long  value)

Sets the number of features tried as possible splits by decision forest regression training algorithm. If 0 then p/3 is used, where p is the total number of features.

Parameters
valueNumber of features
void setImpurityThreshold ( double  value)

Sets threshold value used as stopping criteria

Parameters
valueImpurity threshold
void setMaxTreeDepth ( long  value)

Sets maximal tree depth. Default is 0 (unlimited)

Parameters
valueMaximal tree depth
void setMinObservationsInLeafNode ( long  value)

Sets minimal number of samples per node. Default is 5

Parameters
valueMinimal number of samples
void setNTrees ( long  nTrees)

Sets the number of trees to be created by the decision forest regression training algorithm

Parameters
nTreesNumber of trees
void setObservationsPerTreeFraction ( double  value)

Sets fraction of observations used for a training of one tree, 0 to 1

Parameters
valueFraction of observations
void setResultsToCompute ( long  resultsToCompute)

Sets the 64 bit integer flag that indicates the results to compute

Parameters
resultsToComputeThe 64 bit integer flag that indicates the results to compute
void setSeed ( int  seed)
Deprecated:
This item will be removed in a future release.

Sets the seed for the random numbers generator used by the algorithm

Parameters
seedSeed for the random numbers generator used by the algorithm
void setVariableImportanceMode ( VariableImportanceModeId  value)

Sets the variable importance computation mode

Parameters
valueVariable importance computation mode

The documentation for this class was generated from the following file:

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