C++ API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1
BrownBoost algorithm parameters. More...
Parameter | ( | ) |
Default constructor
Parameter | ( | services::SharedPtr< classifier::training::Batch > | wlTrainForParameter, |
services::SharedPtr< classifier::prediction::Batch > | wlPredictForParameter, | ||
double | acc = 0.3 , |
||
size_t | maxIter = 10 , |
||
double | nrAcc = 1.0e-3 , |
||
size_t | nrMaxIter = 100 , |
||
double | dcThreshold = 1.0e-2 |
||
) |
Constructs BrownBoost parameter structure
[in] | wlTrainForParameter | Pointer to the training algorithm of the weak learner |
[in] | wlPredictForParameter | Pointer to the prediction algorithm of the weak learner |
[in] | acc | Accuracy of the BrownBoost training algorithm |
[in] | maxIter | Maximal number of iterations of the BrownBoost training algorithm |
[in] | nrAcc | Accuracy threshold for Newton-Raphson iterations in the BrownBoost training algorithm |
[in] | nrMaxIter | Maximal number of Newton-Raphson iterations in the BrownBoost training algorithm |
[in] | dcThreshold | Threshold needed to avoid degenerate cases in the BrownBoost training algorithm |
double accuracyThreshold |
Accuracy of the BrownBoost training algorithm
double degenerateCasesThreshold |
Threshold needed to avoid degenerate cases in the BrownBoost training algorithm
size_t maxIterations |
Maximal number of iterations of the BrownBoost training algorithm
double newtonRaphsonAccuracyThreshold |
Accuracy threshold for Newton-Raphson iterations in the BrownBoost training algorithm
size_t newtonRaphsonMaxIterations |
Maximal number of Newton-Raphson iterations in the BrownBoost training algorithm
services::SharedPtr<classifier::prediction::Batch> weakLearnerPrediction |
The algorithm for prediction based on a weak learner model
services::SharedPtr<classifier::training::Batch> weakLearnerTraining |
The algorithm for weak learner model training
For more complete information about compiler optimizations, see our Optimization Notice.