PolynomialRegressionModel Class

public class PolynomialRegressionModel : LinearRegressionModel

Public Class PolynomialRegressionModel
	Inherits LinearRegressionModel

public ref class PolynomialRegressionModel : public LinearRegressionModel

type PolynomialRegressionModel =  
    class
        inherit LinearRegressionModel
    end

The PolynomialRegressionModel type exposes the following members.

	Name	Description
	PolynomialRegressionModel(VectorDouble, VectorDouble, Int32)	Constructs a new PolynomialRegressionModel.
	PolynomialRegressionModel(IDataFrame, String, String, Int32)	Constructs a new PolynomialRegressionModel.

	Name	Description
	AdjustedRSquared	Gets the adjusted R Squared value for the regression. (Inherited from RegressionModelT.)
	AnovaTable	Gets the AnovaTable that summarizes the results of this model. (Inherited from RegressionModelT.)
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	CoefficientOfVariation	Gets the coefficient of variation for the regression. (Inherited from LinearRegressionModel.)
	Computed	Obsolete. Gets whether the model has been computed. (Inherited from Model.)
	CovarianceMatrix	Gets the covariance matrix of the model parameters. (Inherited from RegressionModelT.)
	Data	Gets an object that contains all the data used as input to the model. (Inherited from Model.)
	Degree	Gets the degree of the regression polynomial.
	DegreesOfFreedom	Gets the total degrees of freedom of the data. (Inherited from RegressionModelT.)
	DependentVariable	Gets a vector that contains the dependent variable that is to be fitted. (Inherited from RegressionModelT.)
	Fitted	Gets whether the model has been computed. (Inherited from Model.)
	FStatistic	Gets the F statistic for the regression. (Inherited from RegressionModelT.)
	IndependentVariables	Gets a matrix whose columns contain the independent variables in the model. (Inherited from RegressionModelT.)
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	Leverage	Returns the leverage of each observation. (Inherited from LinearRegressionModel.)
	LogLikelihood	Gets the log-likelihood that the model generated the data. (Inherited from RegressionModelT.)
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	ModelSchema	Gets the collection of variables used in the model. (Inherited from Model.)
	NoIntercept	Gets or sets whether to include the intercept or constant term in the regression model. (Inherited from LinearRegressionModel.)
	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. (Inherited from RegressionModelT.)
	ParameterValues	Gets the values of the parameters associated with this model. (Inherited from RegressionModelT.)
	Predictions	Gets a vector containing the model's predicted values for the dependent variable. (Inherited from RegressionModelT.)
	PValue	Gets the probability corresponding to the F statistic for the regression. (Inherited from RegressionModelT.)
	Residuals	Gets a vector containing the residuals of the model. (Inherited from RegressionModelT.)
	ResidualSumOfSquares	Gets the sum of squares of the residuals of the model. (Inherited from RegressionModelT.)
	RidgeParameter	Gets or sets the coefficient of the squared norm of the regression parameters for ridge regression. (Inherited from LinearRegressionModel.)
	RSquared	Gets the R Squared value for the regression. (Inherited from RegressionModelT.)
	StandardError	Gets the standard error of the regression. (Inherited from RegressionModelT.)
	Standardize	Obsolete. Gets or sets whether the variables should be standardized prior to computing the regression. (Inherited from LinearRegressionModel.)
	Status	Gets the status of the model, which determines which information is available. (Inherited from Model.)
	StepwiseOptions	Gets or sets an object that specifies options for performing stepwise regression. (Inherited from LinearRegressionModel.)
	SupportsWeights	Indicates whether the model supports case weights. (Inherited from LinearRegressionModel.)
	VarianceInflationFactors	Returns the Variance Inflation Factor (VIF) for each variable in the model. (Inherited from LinearRegressionModel.)
	Weights	Gets or sets the actual weights. (Inherited from Model.)

	Name	Description
	Compute	Obsolete. Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Obsolete. Computes the model. (Inherited from Model.)
	Contains	Returns whether another RegressionModelT is nested within this instance. (Inherited from RegressionModelT.)
	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(ParallelOptions)	Fits the model to the data. (Inherited from Model.)
	FitCore	Computes the model to the specified input using the specified parallelization options. (Overrides LinearRegressionModelFitCore(ModelInput, ParallelOptions).)
	GetAkaikeInformationCriterion	Returns the Akaike information criterion (AIC) value for the model. (Inherited from RegressionModelT.)
	GetBayesianInformationCriterion	Returns the Bayesian information criterion (BIC) value for the model. (Inherited from RegressionModelT.)
	GetBreuschGodfreyTest	Gets the Breusch-Godfrey test for serial correlation in the residuals of the regression model. (Inherited from LinearRegressionModel.)
	GetConfidenceBandwidth(Double)	Gets the width of the 95% confidence band around the best-fit curve at the specified point.
	GetConfidenceBandwidth(VectorDouble)	Gets the width of the 95% confidence band around the best-fit curve at the specified point. (Inherited from LinearRegressionModel.)
	GetConfidenceBandwidth(Double, Double)	Gets the width of the confidence band around the best-fit curve at the specified point.
	GetConfidenceBandwidth(VectorDouble, Double)	Gets the width of the confidence band around the best-fit curve at the specified point. (Inherited from LinearRegressionModel.)
	GetCooksDistance	Returns Cook's distance for each of the observations. (Inherited from LinearRegressionModel.)
	GetDeletedResiduals	Returns the deleted residual for each observation (Inherited from LinearRegressionModel.)
	GetDffits	Returns the DFFITS value for each of the observations. (Inherited from LinearRegressionModel.)
	GetDurbinWatsonStatistic	Gets the Durbin-Watson statistic for the residuals of the regression. (Inherited from LinearRegressionModel.)
	GetExternallyStudentizedResiduals	Returns the externally studentized residual for each observation. (Inherited from LinearRegressionModel.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetNormalityOfResidualsTest	Returns a test to verify that the residuals follow a normal distribution. (Inherited from LinearRegressionModel.)
	GetNormalityOfResidualsTest(TestOfNormality)	Returns a test to verify that the residuals follow a normal distribution. (Inherited from LinearRegressionModel.)
	GetPredictionBandwidth(Double)	Gets the width of the prediction band around the best-fit curve at the specified point.
	GetPredictionBandwidth(VectorDouble)	Gets the width of the prediction band around the best-fit curve at the specified point. (Inherited from LinearRegressionModel.)
	GetPredictionBandwidth(Double, Double)	Gets the width of the prediction band around the best-fit curve at the specified point.
	GetPredictionBandwidth(VectorDouble, Double)	Gets the width of the prediction band around the best-fit curve at the specified point. (Inherited from LinearRegressionModel.)
	GetRegressionPolynomial	Returns the regression polynomial as a Polynomial object.
	GetStudentizedDeletedResiduals	Returns the studentized deleted residual for each observation (Inherited from LinearRegressionModel.)
	GetStudentizedResiduals	Returns the studentized residual for each observation. (Inherited from LinearRegressionModel.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Predict(Double)	Predicts the value of the dependent variable based on the specified value of the independent variable.
	Predict(IDataFrame, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModelT.)
	Predict(MatrixT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModelT.)
	Predict(VectorT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModelT.)
	PredictCore(MatrixT, Boolean)	Predicts the value of the dependent variable based on the specified values of the features. (Inherited from RegressionModelT.)
	PredictCore(VectorT, Boolean)	Predicts the value of the dependent variable based on the specified values of the features. (Inherited from RegressionModelT.)
	ResetComputation	Obsolete. Clears all fitted model parameters. (Inherited from Model.)
	ResetFit	Clears all fitted model parameters. (Inherited from Model.)
	SetDataSource	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(SummaryOptions)	Returns a string containing a human-readable summary of the object using the specified options. (Inherited from RegressionModelT.)
	ToString	Returns a string that represents the current object. (Inherited from Model.)

Use the PolynomialRegressionModel class to represent a linear regression model that uses polynomials in one variable.

PolynomialRegressionModel inherits from LinearRegressionModel, but has special constructors that make it easier to create polynomial regression models in one variable. It also defines some members that may be more appropriate for the simple case. For example, the GetRegressionPolynomial method returns a Polynomial object that represents the resulting regression curve.

Reference