Batch Processing

Linear and ridge regressions in the batch processing mode follow the general workflow described in Usage Model: Training and Prediction.

Training

For a description of the input and output, refer to Usage Model: Training and Prediction.

The following table lists parameters of linear and ridge regressions at the training stage. Some of these parameters or their values are specific to a linear or ridge regression algorithm.

Parameter

Algorithm

Default Value

Description

algorithmFPType

any

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

linear regression

defaultDense

Available methods for linear regression training:

  • defaultDense - the normal equations method
  • qrDense - the method based on QR decomposition

ridge regression

Default computation method used by the ridge regression. The only method supported at the training stage is the normal equations method.

ridgeParameters

ridge regression

Numeric table of size 1 x 1 that contains the default ridge parameter equal to 1.

The numeric table of size 1 x k (k is the number of dependent variables) or 1 x 1. The contents of the table depend on its size:

  • size = 1 x k: values of the ridge parameters λj for j = 1, …, k.
  • size = 1 x 1: the value of the ridge parameter for each dependent variable λ1 = ... = λk.

This parameter can be an object of any class derived from NumericTable, except for PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

interceptFlag

any

true

A flag that indicates a need to compute β0j.

Prediction

For a description of the input and output, refer to Usage Model: Training and Prediction.

At the prediction stage, linear and ridge regressions have the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

Default performance-oriented computation method, the only method supported by the regression based prediction.

For more complete information about compiler optimizations, see our Optimization Notice.
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