C++ API Reference for Intel® Data Analytics Acceleration Library 2019
Parameters for the decision forest algorithm. More...
| bool bootstrap |
If true then training set for a tree is a bootstrap of the whole training set
| 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. If 0 then sqrt(p) for classification, p/3 for regression, where p is the total number of features.
| double impurityThreshold |
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.
| size_t maxTreeDepth |
Maximal tree depth. Default is 0 (unlimited)
| bool memorySavingMode |
If true then use memory saving (but slower) mode
| size_t minObservationsInLeafNode |
Minimal number of observations in a leaf node. Default is 1 for classification, 5 for regression.
| size_t nTrees |
Number of trees in the forest. Default is 10
| double observationsPerTreeFraction |
Fraction of observations used for a training of one tree, 0 to 1. Default is 1 (sampling with replacement)
| DAAL_UINT64 resultsToCompute |
64 bit integer flag that indicates the results to compute
| size_t seed |
Seed for the random numbers generator used by the algorithms
| VariableImportanceMode varImportance |
Variable importance computation mode
For more complete information about compiler optimizations, see our Optimization Notice.