Batch Processing

Linear 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.

At the training stage, linear regression has the following parameters:

Parameter	Default Value	Description
algorithmFPType	double	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
method	defaultDense	Available methods for linear regression training: defaultDense - the normal equations method qrDense - the method based on QR decomposition
interceptFlag	true	A flag that indicates a need to compute β_0j.

Parameter

Default Value

Description

algorithmFPType

double

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

method

defaultDense

Available methods for linear regression training:

interceptFlag

true

A flag that indicates a need to compute β_0j.

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

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

Parameter	Default Value	Description
algorithmFPType	double	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.
interceptFlag	true	A flag that indicates a need to compute `β`_0j.

C++:

Java*: