Logistic Regression Model Class
Definition
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
public class LogisticRegressionModel : ClassificationModel<double>
- Inheritance
- Object → Model → ClassificationModel<Double> → LogisticRegressionModel
Remarks
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.
Constructors
Logistic | Constructs a new LogisticRegressionModel. |
Logistic | Constructs a new LogisticRegressionModel. |
Logistic | Constructs a new LogisticRegressionModel. |
Logistic | Constructs a new SimpleRegressionModel. |
Logistic | Constructs a new LogisticRegressionModel. |
Logistic | Constructs a fitted logistic regression model. |
Properties
Base |
Gets an index containing the keys of the columns
that are required inputs to the model.
(Inherited from Model) |
Can |
Gets whether the classifier supports predicting probabilities
for each class.
(Inherited from ClassificationModel<T>) |
Category |
Gets the category index of the dependent variable or targets.
(Inherited from ClassificationModel<T>) |
Computed |
Gets whether the model has been computed.
(Inherited from Model) Obsolete. |
Convergence | Gets the convergence status of the algorithm that computes the model parameters. |
Covariance |
Gets the covariance matrix of the model parameters.
Obsolete. |
Data |
Gets an object that contains all the data used as input to the model.
(Inherited from Model) |
Dependent |
Gets a vector that contains the dependent variable that is to be fitted.
(Inherited from ClassificationModel<T>) |
Fitted |
Gets whether the model has been computed.
(Inherited from Model) |
Independent |
Gets a matrix whose columns contain the independent variables in the model.
(Inherited from ClassificationModel<T>) |
Input |
Gets the schema for the features used for fitting the model.
(Inherited from Model) |
Log | Gets the log-likelihood of the fitted model. |
Max |
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. |
Model |
Gets the collection of variables used in the model.
(Inherited from Model) |
Number |
Gets the number of observations the model is based on.
(Inherited from Model) |
Parallel |
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. |
Parameter | Gets the collection of parameters associated with this model. |
Predicted |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel<T>) |
Predicted |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel<T>) |
Predictions |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel<T>) |
Probability |
Gets a matrix containing the residuals of the model.
(Inherited from ClassificationModel<T>) |
Status |
Gets the status of the model, which determines which information is available.
(Inherited from Model) |
Supports |
Indicates whether the model supports case weights.
(Inherited from Model) |
Weights |
Gets or sets the actual weights.
(Inherited from Model) |
Methods
Compute() |
Computes the model.
(Inherited from Model) Obsolete. |
Compute( |
Computes the model.
(Inherited from Model) Obsolete. |
Contains |
Returns whether another ClassificationModel<T> is nested
within this instance.
(Inherited from ClassificationModel<T>) |
Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object) |
Fit() |
Fits the model to the data.
(Inherited from Model) |
Fit( |
Fits the model to the data.
(Inherited from Model) |
Fit |
Fits the model to the data.
(Overrides Model.FitCore(ModelInput, ParallelOptions)) |
Get | Returns the Akaike information criterion (AIC) value for the model. |
Get | Returns the log-likelihood of the model containing only a constant term. |
Get | Returns the Bayesian information criterion (BIC) value for the model. |
Get | Returns the Cox & Snell pseudo R-squared value of the model. |
GetHashCode | Serves as the default hash function. (Inherited from Object) |
Get | Calculates the information matrix for the regression. |
Get | Returns a test to verify the significance of the logistic model. |
Get | Returns a test to verify the significance of the logistic model. |
Get | Returns the McFadden pseudo R-squared value of the model. |
Get | Returns the Nagelkerke pseudo R-squared value of the model. |
Get | Returns the Wald test for all the parameters in the regression. |
GetType | Gets the Type of the current instance. (Inherited from Object) |
Get | Returns the Wald test for all the parameters in the regression. |
Get | Returns the Wald test for the selected parameters in the regression. |
MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) |
Predict( |
Predicts the most likely class based on
the specified features.
(Inherited from ClassificationModel<T>) |
Predict( |
Predicts the value of the output corresponding to
the specified features.
(Inherited from ClassificationModel<T>) |
Predict( |
Predicts the value of the output corresponding to
the specified input.
(Inherited from ClassificationModel<T>) |
Predict |
Predicts the value of the dependent variable based on
the specified values of the features.
(Overrides ClassificationModel<T>.PredictCore(Matrix<T>, Boolean)) |
Predict |
Predicts the class based on
the specified values of the features.
(Overrides ClassificationModel<T>.PredictCore(Vector<T>, Boolean)) |
Predict |
Predicts the value of the output corresponding to
the specified input.
(Inherited from ClassificationModel<T>) |
Predict |
Predicts the value of the output corresponding to
the specified features.
(Inherited from ClassificationModel<T>) |
Predict |
Predicts the value of the output corresponding to
the specified input.
(Inherited from ClassificationModel<T>) |
Predict |
Predicts the probabilities of each class based on
the specified values of the features.
(Overrides ClassificationModel<T>.PredictProbabilitiesCore(Matrix<T>, Matrix<Double>, Boolean)) |
Predict |
Predicts the probabilities of each class based on
the specified values of the features.
(Overrides ClassificationModel<T>.PredictProbabilitiesCore(Vector<T>, Vector<Double>, Boolean)) |
Predict |
Predicts the value of the output corresponding to
the specified input.
(Inherited from ClassificationModel<T>) |
Predict |
Predicts the value of the output corresponding to
the specified features.
(Inherited from ClassificationModel<T>) |
Predict |
Predicts the probabilities of each class based on
the specified features.
(Inherited from ClassificationModel<T>) |
Reset |
Clears all fitted model parameters.
(Inherited from Model) Obsolete. |
Reset |
Clears all fitted model parameters.
(Inherited from Model) |
Set |
Uses the specified data frame as the source for all input variables.
(Inherited from Model) |
Summarize() |
Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model) |
Summarize( |
Returns a string containing a human-readable summary of the object using the specified options.
(Overrides Model.Summarize(SummaryOptions)) |
ToString | Returns a string that represents the current object. (Inherited from Model) |