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

Public Member Functions | Static Public Attributes | List of all members
interface2_Parameter Class Reference

LogitBoost algorithm parameters. More...

Public Member Functions

def __init__
 
def check
 
- Public Member Functions inherited from interface2_Parameter
def __init__
 
def check
 
- Public Member Functions inherited from Parameter
def __init__
 
def check
 

Static Public Attributes

 weakLearnerTraining = ...
 
 weakLearnerPrediction = ...
 
 accuracyThreshold = ...
 
 maxIterations = ...
 
 weightsDegenerateCasesThreshold = ...
 
 responsesDegenerateCasesThreshold = ...
 
- Static Public Attributes inherited from interface2_Parameter
 nClasses = ...
 
 resultsToEvaluate = ...
 

Detailed Description

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(interface2_Parameter self) -> Status

Member Data Documentation

accuracyThreshold = ...
static

Accuracy of the LogitBoost training algorithm

maxIterations = ...
static

Maximal number of terms in additive regression

responsesDegenerateCasesThreshold = ...
static

Threshold to avoid degenerate cases when calculating responses Z

weakLearnerPrediction = ...
static

The algorithm for prediction based on a weak learner model

weakLearnerTraining = ...
static

The algorithm for weak learner model training

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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