Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

Public Member Functions | Static Public Attributes | List of all members
Parameter Class Reference

LogitBoost algorithm parameters. More...

Public Member Functions

def __init__
 
def check
 
- Public Member Functions inherited from Parameter
def __init__
 
def check
 
- Public Member Functions inherited from Parameter
def __init__
 
def check
 
- Public Member Functions inherited from Parameter
def __init__
 
def check
 

Static Public Attributes

 accuracyThreshold = ...
 
 maxIterations = ...
 
 nClasses = ...
 
 weightsDegenerateCasesThreshold = ...
 
 responsesDegenerateCasesThreshold = ...
 
- Static Public Attributes inherited from Parameter
 weakLearnerTraining = ...
 
 weakLearnerPrediction = ...
 
- Static Public Attributes inherited from Parameter
 nClasses = ...
 

Detailed Description

Deprecated:
This item will be removed in a future release.

Constructor & Destructor Documentation

def __init__ (   self,
  args 
)

Variant 1
Default constructor

Variant 2

Constructs LogitBoost parameter structure

Parameters
wlTrainForParameterPointer to the training algorithm of the weak learner
wlPredictForParameterPointer to the prediction algorithm of the weak learner
accAccuracy of the LogitBoost training algorithm
maxIterMaximal number of terms in additive regression
nCNumber of classes in the training data set
wThrThreshold to avoid degenerate cases when calculating weights W
zThrThreshold to avoid degenerate cases when calculating responses Z

Variant 3

Constructs LogitBoost parameter structure

Parameters
wlTrainForParameterPointer to the training algorithm of the weak learner
wlPredictForParameterPointer to the prediction algorithm of the weak learner
accAccuracy of the LogitBoost training algorithm
maxIterMaximal number of terms in additive regression
nCNumber of classes in the training data set
wThrThreshold to avoid degenerate cases when calculating weights W
zThrThreshold to avoid degenerate cases when calculating responses Z

Variant 4

Constructs LogitBoost parameter structure

Parameters
wlTrainForParameterPointer to the training algorithm of the weak learner
wlPredictForParameterPointer to the prediction algorithm of the weak learner
accAccuracy of the LogitBoost training algorithm
maxIterMaximal number of terms in additive regression
nCNumber of classes in the training data set
wThrThreshold to avoid degenerate cases when calculating weights W
zThrThreshold to avoid degenerate cases when calculating responses Z

Variant 5

Constructs LogitBoost parameter structure

Parameters
wlTrainForParameterPointer to the training algorithm of the weak learner
wlPredictForParameterPointer to the prediction algorithm of the weak learner
accAccuracy of the LogitBoost training algorithm
maxIterMaximal number of terms in additive regression
nCNumber of classes in the training data set
wThrThreshold to avoid degenerate cases when calculating weights W
zThrThreshold to avoid degenerate cases when calculating responses Z

Variant 6

Constructs LogitBoost parameter structure

Parameters
wlTrainForParameterPointer to the training algorithm of the weak learner
wlPredictForParameterPointer to the prediction algorithm of the weak learner
accAccuracy of the LogitBoost training algorithm
maxIterMaximal number of terms in additive regression
nCNumber of classes in the training data set
wThrThreshold to avoid degenerate cases when calculating weights W
zThrThreshold to avoid degenerate cases when calculating responses Z

Variant 7

Constructs LogitBoost parameter structure

Parameters
wlTrainForParameterPointer to the training algorithm of the weak learner
wlPredictForParameterPointer to the prediction algorithm of the weak learner
accAccuracy of the LogitBoost training algorithm
maxIterMaximal number of terms in additive regression
nCNumber of classes in the training data set
wThrThreshold to avoid degenerate cases when calculating weights W
zThrThreshold to avoid degenerate cases when calculating responses Z

Member Function Documentation

def check (   self)

check(Parameter self) -> Status

Member Data Documentation

accuracyThreshold = ...
static

Accuracy of the LogitBoost training algorithm

maxIterations = ...
static

Maximal number of terms in additive regression

nClasses = ...
static

Number of classes

responsesDegenerateCasesThreshold = ...
static

Threshold to avoid degenerate cases when calculating responses Z

weightsDegenerateCasesThreshold = ...
static

Threshold to avoid degenerate cases when calculating weights W


The documentation for this class was generated from the following file:

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