C++ API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1
Parameters for the gradient boosted trees algorithm. More...
engines::EnginePtr engine |
Engine for the random numbers generator used by the algorithms
size_t featuresPerNode |
Number of features tried as possible splits per node. Range : [0, p] where p is the total number of features. Default is 0 (use all features)
int internalOptions |
Internal options
double lambda |
L2 regularization parameter on weights. Range: [0, inf). Default is 1
size_t maxBins |
Used with 'inexact' split finding method only. Maximal number of discrete bins to bucket continuous features. Default is 256. Increasing the number results in higher computation costs
size_t maxIterations |
Maximal number of iterations of the gradient boosted trees training algorithm. Default is 50
size_t maxTreeDepth |
Maximal tree depth, 0 for unlimited. Default is 6
bool memorySavingMode |
If true then use memory saving (but slower) mode. Default is false
size_t minBinSize |
Used with 'inexact' split finding method only. Minimal number of observations in a bin. Default is 5
size_t minObservationsInLeafNode |
Minimal number of observations in a leaf node. Default is 5.
double minSplitLoss |
Loss regularization parameter. Min loss reduction required to make a further partition on a leaf node of the tree. Range: [0, inf). Default is 0
double observationsPerTreeFraction |
Fraction of observations used for a training of one tree, sampling without replacement. Range: (0, 1]. Default is 1 (no sampling, entire dataset is used)
double shrinkage |
Learning rate of the boosting procedure. Scales the contribution of each tree by a factor (0, 1]. Default is 0.3
SplitMethod splitMethod |
Split finding method. Default is exact
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