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## LogisticRegressionModel Class | Extreme Optimization Numerical Libraries for .NET Professional |

Extreme.DataAnalysis.Models

Extreme.DataAnalysis.Models

Extreme.Statistics

**Namespace:**Extreme.Statistics

**Assembly:**Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1

The LogisticRegressionModel type exposes the following members.

Name | Description | |
---|---|---|

BaseFeatureIndex |
Gets an index containing the keys of the columns
that are required inputs to the model.
(Inherited from Model.) | |

CanPredictProbabilities |
Gets whether the classifier supports predicting probabilities
for each class.
(Inherited from ClassificationModel | |

CategoryIndex |
Gets the category index of the dependent variable or targets.
(Inherited from ClassificationModel | |

Computed | Obsolete.
Gets whether the model has been computed.
(Inherited from Model.) | |

ConvergenceStatus |
Gets the convergence status of the algorithm that computes the model parameters.
| |

CovarianceMatrix | Obsolete.
Gets the covariance matrix of the model parameters.
| |

Data |
Gets an object that contains all the data used as input to the model.
(Inherited from Model.) | |

DependentVariable |
Gets a vector that contains the dependent variable that is to be fitted.
(Inherited from ClassificationModel | |

Fitted |
Gets whether the model has been computed.
(Inherited from Model.) | |

IndependentVariables |
Gets a matrix whose columns contain the independent variables in the model.
(Inherited from ClassificationModel | |

InputSchema |
Gets the schema for the features used for fitting the model.
(Inherited from Model.) | |

LogLikelihood |
Gets the log-likelihood of the fitted model.
| |

MaxDegreeOfParallelism |
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model.) | |

Method |
Gets or sets the kind of logistic regression represented by this LogisticRegressionModel.
| |

ModelSchema |
Gets the collection of variables used in the model.
(Inherited from Model.) | |

NumberOfObservations |
Gets the number of observations the model is based on.
(Inherited from Model.) | |

ParallelOptions |
Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model.) | |

Parameters |
Gets the collection of parameters associated with this model.
| |

ParameterValues |
Gets the collection of parameters associated with this model.
| |

PredictedLogProbabilities |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel | |

PredictedProbabilities |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel | |

Predictions |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel | |

ProbabilityResiduals |
Gets a matrix containing the residuals of the model.
(Inherited from ClassificationModel | |

Status |
Gets the status of the model, which determines which information is available.
(Inherited from Model.) | |

SupportsWeights |
Indicates whether the model supports case weights.
(Inherited from Model.) | |

Weights |
Gets or sets the actual weights.
(Inherited from Model.) |

Use the LogisticRegressionModel class to analyze a situation where the outcome can have two or more possible values. A logistic regression model tries to express one variable, called the dependent variable, which can have only two distinct values, as a function of one or more other variables called independent variables or predictors in a specific form.

Logistic regression is a special case of a GeneralizedLinearModel with a binomial distribution and the logit link function. To perform variants of logistic regression, like probit regression, use the GeneralizedLinearModel class.

In addition to binary logistic regression, the LogisticRegressionModel can also represent multinomial logistic regression, where there may be more than two outcomes. In this case, the dependent variable must be a ICategoricalVector.

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