Base class for parameters of the decision forest regression training algorithm.
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◆ getFeaturesPerNode()
long getFeaturesPerNode |
( |
| ) |
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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
◆ getImpurityThreshold()
double getImpurityThreshold |
( |
| ) |
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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
◆ getMaxTreeDepth()
Returns maximal tree depth. Default is 0 (unlimited)
- Returns
- Maximal tree depth
◆ getMinObservationsInLeafNode()
long getMinObservationsInLeafNode |
( |
| ) |
|
Returns minimal number of samples per node. Default is 5
- Returns
- Minimal number of samples
◆ getNTrees()
Returns number of trees to be created by the decision forest regression training algorithm
- Returns
- Number of trees
◆ getObservationsPerTreeFraction()
double getObservationsPerTreeFraction |
( |
| ) |
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Returns fraction of observations used for a training of one tree, 0 to 1. Default is 1 (sampling with replacement)
- Returns
- Fraction of observations
◆ getResultsToCompute()
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
◆ 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
◆ getVariableImportanceMode()
Returns variable importance computation mode
- Returns
- Variable importance computation mode
◆ setEngine()
Sets the engine to be used by the algorithm
- Parameters
-
engine | to be used by the algorithm |
◆ setFeaturesPerNode()
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
-
◆ setImpurityThreshold()
void setImpurityThreshold |
( |
double |
value | ) |
|
Sets threshold value used as stopping criteria
- Parameters
-
◆ setMaxTreeDepth()
void setMaxTreeDepth |
( |
long |
value | ) |
|
Sets maximal tree depth. Default is 0 (unlimited)
- Parameters
-
◆ setMinObservationsInLeafNode()
void setMinObservationsInLeafNode |
( |
long |
value | ) |
|
Sets minimal number of samples per node. Default is 5
- Parameters
-
value | Minimal number of samples |
◆ setNTrees()
void setNTrees |
( |
long |
nTrees | ) |
|
Sets the number of trees to be created by the decision forest regression training algorithm
- Parameters
-
◆ setObservationsPerTreeFraction()
void setObservationsPerTreeFraction |
( |
double |
value | ) |
|
Sets fraction of observations used for a training of one tree, 0 to 1
- Parameters
-
value | Fraction of observations |
◆ setResultsToCompute()
void setResultsToCompute |
( |
long |
resultsToCompute | ) |
|
Sets the 64 bit integer flag that indicates the results to compute
- Parameters
-
resultsToCompute | The 64 bit integer flag that indicates the results to compute |
◆ setSeed()
- Deprecated:
- This item will be removed in a future release.
Sets the seed for the random numbers generator used by the algorithm
- Parameters
-
seed | Seed for the random numbers generator used by the algorithm |
◆ setVariableImportanceMode()
Sets the variable importance computation mode
- Parameters
-
value | Variable importance computation mode |
The documentation for this class was generated from the following file:
- decision_forest/regression/training/Parameter.java