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Regression Usage Model

A typical workflow for regression methods includes training and prediction, as explained below.

Algorithm-Specific Parameters

The parameters used by regression algorithms at each stage depend on a specific algorithm. For a list of these parameters, refer to the description of an appropriate regression algorithm.

Training Stage

Regression Usage Model: Training Stage

At the training stage, regression algorithms accept the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Training Input for Regression Algorithms

Input ID	Input
data	Pointer to the \(n \times p\) numeric table with the training data set. This table can be an object of any class derived from NumericTable.
weights	Weights of the observations in the training data set. Optional argument.
dependentVariables	Pointer to the \(n \times k\) numeric table with responses (\(k\) dependent variables). This table can be an object of any class derived from NumericTable except PackedSymmetricMatrix and PackedTriangularMatrix.

At the training stage, regression algorithms calculate the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Training Output for Regression Algorithms

Result ID	Result
model	Pointer to the regression model being trained. The result can only be an object of the Model class.

Prediction Stage

Regression Usage Model: Prediction Stage

At the prediction stage, regression algorithms accept the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Prediction Input for Regression Algorithms

Input ID	Input
data	Pointer to the \(n \times p\) numeric table with the working data set. This table can be an object of any class derived from NumericTable.
model	Pointer to the trained regression model. This input can only be an object of the Model class.

At the prediction stage, regression algorithms calculate the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Prediction Output for Regression Algorithms

Result ID	Result
prediction	Pointer to the \(n \times k\) numeric table with responses (\(k\) dependent variables). By default, this table is an object of the HomogenNumericTable class, but you can define it as an object of any class derived from NumericTable except PackedSymmetricMatrix and PackedTriangularMatrix.

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