CreateBoostClassifier
Create a Boost classifier. The initial reference count is set to one if a new model is created and kept, otherwise. The Boost classifier is only applicable to binary classification problems. It learns to distinguish between samples labelled with two user-defined class labels by incrementally adding weak classifiers to improve the classification results. Models of this type neither support on-line training (sample by sample) nor retraining.
Syntax
Definition:
HRESULT CreateBoostClassifier(
HRESULT hrPrev,
ITcVnMlModel*& ipMlModel,
ETcVnBoostClassifierType eType = BCT_REAL,
ULONG nMaxDepth = 1,
ULONG nMinSamples = 10,
ULONG nWeakClassifiers = 100,
double fWeightTrimRate = 0.95,
ITcVnContainer* ipClassPriors = nullptr
)Parameters
|
Name |
Type |
Default |
Description |
|---|---|---|---|
|
hrPrev |
|
HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.) | |
|
ipMlModel |
|
Returns the created model (Non-zero interface pointers are reused.) | |
|
eType |
BCT_REAL |
Learning algorithm type (default: TCVN_BCT_REAL) | |
|
nMaxDepth |
ULONG |
1 |
Maximum tree depth (default: 1) |
|
nMinSamples |
ULONG |
10 |
Minimum number of samples within a node required for splitting (default: 10) |
|
nWeakClassifiers |
ULONG |
100 |
Number of weak classifiers (default: 100) |
|
fWeightTrimRate |
double |
0.95 |
Weight threshold used during training (off: 0; default: 0.95). |
|
ipClassPriors |
nullptr |
Class priors (ContainerType_Vector_REAL or ContainerType_Vector_LREAL; only for classifiers; optional, set to 0 if not required or not allowed; default: 0) |
Return valueRequired License
TC3 Machine Learning Realtime Inference