PartialLeastSquaresModel Class

public class PartialLeastSquaresModel : Model

Public Class PartialLeastSquaresModel
	Inherits Model

public ref class PartialLeastSquaresModel : public Model

type PartialLeastSquaresModel =  
    class
        inherit Model
    end

The PartialLeastSquaresModel type exposes the following members.

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

	Name	Description
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	Coefficients	Gets a matrix containing the (unstandardized) coefficients of the Partial Least Squares regression.
	Computed	Obsolete. Gets whether the model has been computed. (Inherited from Model.)
	Data	Gets an object that contains all the data used as input to the model. (Inherited from Model.)
	DependentVariables	Gets a matrix that contains the dependent variables that are to be fitted.
	Fitted	Gets whether the model has been computed. (Inherited from Model.)
	IndependentVariables	Gets a matrix whose columns contain the independent variables in the model.
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	Intercepts	Gets a matrix containing the intercept term of the Partial Least Squares regression.
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	Method	Gets or sets the method used to compute the PLS model.
	ModelSchema	Gets the collection of variables used in the model. (Inherited from Model.)
	NumberOfComponents	Gets or sets the number of components to calculate.
	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.)
	PredictedValues	Gets the residuals of the dependent variables.
	StandardizedCoefficients	Gets a matrix containing the standardized coefficients of the Partial Least Squares regression.
	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.)
	VariableImportanceInProjection	Gets a matrix that contains the Variable Importance in Projection (VIP) value for each variable and each number of components.
	WeightMatrix	Gets the weights of the independent variables.
	Weights	Gets or sets the actual weights. (Inherited from Model.)
	XCumulativeVarianceExplained	Gets a vector containing the cumulative proportion of variance in the independent variables explained by each the component
	XDistanceToModel	Gets a vector containing the distance to the model of the independent variables.
	XLoadings	Gets the scores matrix of the independent variables.
	XResiduals	Gets the residuals of the independent variables.
	XScalingMethod	Gets or sets how independent variables are scaled.
	XScores	Gets the loadings matrix of the independent variables.
	XVarianceExplained	Gets a vector containing the proportion of variance in the independent variables explained by each the component
	YCumulativeVarianceExplained	Gets a vector containing the cumulative proportion of variance in the dependent variables explained by each the component
	YDistanceToModel	Gets a vector containing the distance to the model of the dependent variables.
	YLoadings	Gets the scores matrix of the dependent variables.
	YResiduals	Gets the residuals of the dependent variables.
	YScalingMethod	Gets or sets how dependent variables are scaled.
	YScores	Gets the loadings matrix of the dependent variables.
	YVarianceExplained	Gets a vector containing the proportion of variance in the dependent variables explained by each the component

	Name	Description
	Compute	Obsolete. Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Obsolete. Computes the model. (Inherited from Model.)
	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. (Overrides ModelFitCore(ModelInput, ParallelOptions).)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Predict(IDataFrame, ModelInputFormat)	Predicts the value of the dependent variables corresponding to the specified features.
	Predict(MatrixDouble, ModelInputFormat)	Predicts the value of the dependent variables corresponding to the specified features.
	Predict(VectorDouble, ModelInputFormat)	Predicts the value of the dependent variables corresponding to the specified features.
	PredictCore(MatrixDouble)	Predicts the value of the dependent variable based on the specified values of the features.
	PredictCore(VectorDouble)	Predicts the value of the dependent variable based on the specified values of the features.
	Press	Returns the Predicted REsidual Sum of Squares (PRESS) value for the specified test features and targets.
	ResetComputation	Obsolete. Clears all fitted model parameters. (Inherited from Model.)
	ResetFit	Clears all fitted model parameters. (Inherited from Model.)
	RootMeanPress	Returns the square root of the mean of the Predicted REsidual Sum of Squares (PRESS) value for the specified test features and targets.
	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 Model.)
	ToString	Returns a string that represents the current object. (Inherited from Model.)

Use the PartialLeastSquaresModel to perform a partial least squares calculation.

Partial least squares is a technique that fits combinations of independent variables called factors to one or more dependent variables. The factors are chosen to maximize the covariance between the factors and the dependent variables.

Partial least squares is useful when the number of independent variables is large compared to the number of observations, or when variables are highly correlated.

Fitting the model is done with one of two standard algorithms: NIPALS (Nonlinear Iterative PArtial Least Squares) or SIMPLS (Statistically Inspired Modification of Partial Least Squares). The two algorithms give identical results when there is only one dependent variable.

Reference