Parameters for the gradient boosted trees algorithm.
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Engine for the random numbers generator used by the algorithms
Number of features tried as possible splits per node.
Used with 'inexact' split finding method only.
Maximal number of iterations of the gradient boosted trees training algorithm.
Maximal tree depth, 0 for unlimited. Default is 6
If true then use memory saving (but slower) mode. Default is false
Used with 'inexact' split finding method only.
minObservationsInLeafNode = ... |
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static |
Minimal number of observations in a leaf node. Default is 5.
Loss regularization parameter. Min loss reduction required to make a further partition
observationsPerTreeFraction = ... |
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static |
Fraction of observations used for a training of one tree, sampling without replacement.
Learning rate of the boosting procedure.
Split finding method. Default is exact
The documentation for this class was generated from the following file: