Base class for parameters of the decision tree classification algorithm.
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◆ getMaxTreeDepth()
Returns the maximum tree depth. 0 means unlimited depth.
- Returns
- Maximum tree depth
◆ getMinObservationsInLeafNodes()
long getMinObservationsInLeafNodes |
( |
| ) |
|
Returns the minimum number of observations in the leaf node
- Returns
- Minimum number of observations in the leaf node
◆ getPruning()
Returns the pruning method for decision tree training algorithm
- Returns
- Pruning method for decision tree
◆ getSplitCriterion()
Returns the split criterion for decision tree classification
- Returns
- Split criterion for decision tree classification
◆ setMaxTreeDepth()
void setMaxTreeDepth |
( |
long |
value | ) |
|
Sets the maximum tree depth, 0 means unlimited depth
- Parameters
-
◆ setMinObservationsInLeafNodes()
void setMinObservationsInLeafNodes |
( |
long |
value | ) |
|
Sets the minimum number of observations in the leaf node
- Parameters
-
value | Minimum number of observations in the leaf node |
◆ setPruning()
Sets the pruning method for decision tree training algorithm
- Parameters
-
value | Pruning method for decision tree |
◆ setSplitCriterion()
Sets the split criterion for decision tree classification
- Parameters
-
value | Split criterion for decision tree classification |
The documentation for this class was generated from the following file:
- decision_tree/classification/Parameter.java