LogisticRegressionAnalysis Properties 
The LogisticRegressionAnalysis type exposes the following members.
Name  Description  

Array  Obsolete.
Gets the source matrix from which the analysis was run.
 
ChiSquare 
Gets the ChiSquare (Likelihood Ratio) Test for the model.
 
Coefficients 
Gets the collection of coefficients of the model.
 
CoefficientValues 
Gets the value of each coefficient.
 
ComputeInnerModels 
Gets or sets whether nested models should be computed in
order to calculate the likelihoodratio test of each of
the coefficients. Default is false.
 
Confidences 
Gets the 95% Confidence Intervals (C.I.)
for each coefficient found in the regression.
 
Deviance 
Gets the Deviance of the model.
 
InformationMatrix 
Gets the information matrix obtained during learning.
 
Inputs 
Gets or sets the name of the input variables for the model.
 
Iterations 
Gets or sets the maximum number of iterations to be
performed by the regression algorithm. Default is 50.
 
LikelihoodRatioTests 
Gets the LikelihoodRatio Tests for each coefficient.
 
LogLikelihood 
Gets the LogLikelihood for the model.
 
NumberOfInputs 
Gets the number of inputs accepted by the model.
(Inherited from TransformBaseTInput, TOutput.)  
NumberOfOutputs 
Gets the number of outputs generated by the model.
(Inherited from TransformBaseTInput, TOutput.)  
NumberOfSamples 
Gets the number of samples used to compute the analysis.
 
OddsRatios 
Gets the Odds Ratio for each coefficient
found during the logistic regression.
 
Output 
Gets or sets the name of the output variable for the model.
 
Outputs  Obsolete.
Gets the dependent variable value
for each of the source input points.
 
Regression 
Gets the Logistic Regression model created
and evaluated by this analysis.
 
Regularization 
Gets or sets the regularization value to be
added in the objective function. Default is
1e10.
 
Result  Obsolete.
Gets the resulting probabilities obtained
by the logistic regression model.
 
Source  Obsolete.
Gets the source matrix from which the analysis was run.
 
StandardErrors 
Gets the Standard Error for each coefficient
found during the logistic regression.
 
Token 
Gets or sets a cancellation token that can be used to
stop the learning algorithm while it is running.
 
Tolerance 
Gets or sets the difference between two iterations of the regression
algorithm when the algorithm should stop. The difference is calculated
based on the largest absolute parameter change of the regression. Default
is 1e5.
 
WaldTests 
Gets the Wald Tests for each coefficient.
 
Weights  Obsolete.
Gets the sample weight associated with each input vector.
