Developer Guide for Intel® Data Analytics Acceleration Library 2016 Update 4
LogitBoost classifier follows the general workflow described in Usage Model: Training and Prediction.
For a description of the input and output, refer to Usage Model: Training and Prediction.
At the training stage, a LogitBoost classifier has the following parameters:
|
Parameter |
Default Value |
Description |
|
|---|---|---|---|
|
algorithmFPType |
double |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
|
|
method |
defaultDense |
The computation method used by the LogitBoost classifier. The only training method supported so far is the Friedman method. |
|
|
weakLearnerTraining |
Pointer to an object of the stump training class |
Pointer to the training algorithm of the weak learner. By default, a stump weak learner is used. |
|
|
weakLearnerPrediction |
Pointer to an object of the stump prediction class |
Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used. |
|
|
accuracyThreshold |
0.01 |
LogitBoost training accuracy. |
|
|
maxIterations |
100 |
The maximal number of iterations for the LogitBoost algorithm. |
|
|
nClasses |
Not applicable |
The number of classes, a required parameter. |
|
|
weightsDegenerateCasesThreshold |
1e-10 |
The threshold to avoid degenerate cases when calculating weights wij. |
|
|
responsesDegenerateCasesThreshold |
1e-10 |
The threshold to avoid degenerate cases when calculating responses zij. |
|
For a description of the input and output, refer to Usage Model: Training and Prediction.
At the prediction stage, a LogitBoost classifier has the following parameters:
|
Parameter |
Default Value |
Description |
|
|---|---|---|---|
|
algorithmFPType |
double |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
|
|
method |
defaultDense |
Performance-oriented computation method, the only method supported by the LogitBoost classifier at the prediction stage. |
|
|
weakLearnerPrediction |
Pointer to an object of the stump prediction class |
Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used. |
|
|
nClasses |
Not applicable |
The number of classes, a required parameter. |
|
The algorithm terminates if it achieves the specified accuracy or reaches the specified maximal number of iterations. To determine the actual number of iterations performed, call the getNumberOfWeakLearners() method of the LogitBoostModel class and divide it by nClasses.