Regression Usage Model

Intel® oneAPI Data Analytics Library Developer Guide and Reference

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ID 772611

Date 7/13/2023

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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 $n i m e s p$ 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 $n i m e s k$ 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 $n i m e s p$ 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 $n i m e s k$ 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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Intel® oneAPI Data Analytics Library Developer Guide and Reference

Regression Usage Model

Algorithm-Specific Parameters

Training Stage

Prediction Stage