LogitBoost algorithm parameters.
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- Variant 1
- Default constructor
- Variant 2
Constructs LogitBoost parameter structure
- Parameters
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wlTrainForParameter | Pointer to the training algorithm of the weak learner |
wlPredictForParameter | Pointer to the prediction algorithm of the weak learner |
acc | Accuracy of the LogitBoost training algorithm |
maxIter | Maximal number of terms in additive regression |
nC | Number of classes in the training data set |
wThr | Threshold to avoid degenerate cases when calculating weights W |
zThr | Threshold to avoid degenerate cases when calculating responses Z |
- Variant 3
Constructs LogitBoost parameter structure
- Parameters
-
wlTrainForParameter | Pointer to the training algorithm of the weak learner |
wlPredictForParameter | Pointer to the prediction algorithm of the weak learner |
acc | Accuracy of the LogitBoost training algorithm |
maxIter | Maximal number of terms in additive regression |
nC | Number of classes in the training data set |
wThr | Threshold to avoid degenerate cases when calculating weights W |
zThr | Threshold to avoid degenerate cases when calculating responses Z |
- Variant 4
Constructs LogitBoost parameter structure
- Parameters
-
wlTrainForParameter | Pointer to the training algorithm of the weak learner |
wlPredictForParameter | Pointer to the prediction algorithm of the weak learner |
acc | Accuracy of the LogitBoost training algorithm |
maxIter | Maximal number of terms in additive regression |
nC | Number of classes in the training data set |
wThr | Threshold to avoid degenerate cases when calculating weights W |
zThr | Threshold to avoid degenerate cases when calculating responses Z |
- Variant 5
Constructs LogitBoost parameter structure
- Parameters
-
wlTrainForParameter | Pointer to the training algorithm of the weak learner |
wlPredictForParameter | Pointer to the prediction algorithm of the weak learner |
acc | Accuracy of the LogitBoost training algorithm |
maxIter | Maximal number of terms in additive regression |
nC | Number of classes in the training data set |
wThr | Threshold to avoid degenerate cases when calculating weights W |
zThr | Threshold to avoid degenerate cases when calculating responses Z |
- Variant 6
Constructs LogitBoost parameter structure
- Parameters
-
wlTrainForParameter | Pointer to the training algorithm of the weak learner |
wlPredictForParameter | Pointer to the prediction algorithm of the weak learner |
acc | Accuracy of the LogitBoost training algorithm |
maxIter | Maximal number of terms in additive regression |
nC | Number of classes in the training data set |
wThr | Threshold to avoid degenerate cases when calculating weights W |
zThr | Threshold to avoid degenerate cases when calculating responses Z |
- Variant 7
Constructs LogitBoost parameter structure
- Parameters
-
wlTrainForParameter | Pointer to the training algorithm of the weak learner |
wlPredictForParameter | Pointer to the prediction algorithm of the weak learner |
acc | Accuracy of the LogitBoost training algorithm |
maxIter | Maximal number of terms in additive regression |
nC | Number of classes in the training data set |
wThr | Threshold to avoid degenerate cases when calculating weights W |
zThr | Threshold to avoid degenerate cases when calculating responses Z |
check(interface2_Parameter self) -> Status
Accuracy of the LogitBoost training algorithm
Maximal number of terms in additive regression
responsesDegenerateCasesThreshold = ... |
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Threshold to avoid degenerate cases when calculating responses Z
weakLearnerPrediction = ... |
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The algorithm for prediction based on a weak learner model
weakLearnerTraining = ... |
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The algorithm for weak learner model training
weightsDegenerateCasesThreshold = ... |
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Threshold to avoid degenerate cases when calculating weights W
The documentation for this class was generated from the following file:
- algorithms/logitboost/__init__.py