Parameters for the decision forest algorithm.
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If true then training set for a tree is a bootstrap of the whole training set
Engine for the random numbers generator used by the algorithms
Number of features tried as possible splits per node.
Threshold value used as stopping criteria: if the impurity value in the node is smaller
Maximal tree depth. Default is 0 (unlimited)
If true then use memory saving (but slower) mode
minObservationsInLeafNode = ... |
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static |
Minimal number of observations in a leaf node.
Number of trees in the forest. Default is 10
observationsPerTreeFraction = ... |
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static |
Fraction of observations used for a training of one tree, 0 to 1.
64 bit integer flag that indicates the results to compute
Seed for the random numbers generator used by the algorithms
- Deprecated:
- This item will be removed in a future release.
Use engine instead.
Variable importance computation mode
The documentation for this class was generated from the following file:
- decision_forest/training.py