Developer Guide and Reference

  • 2021.6
  • 04/11/2022
  • Public Content
Contents

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 LaTex Math image. 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 LaTex Math image. numeric table with responses (LaTex Math image. 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 LaTex Math image. 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 LaTex Math image. numeric table with responses (LaTex Math image. 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
.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.