Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 2
Linear and ridge regressions in the batch processing mode follow the general workflow described in Usage Model: Training and Prediction.
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:
|
|
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:
This parameter can be an object of any class derived from NumericTable, except for PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable. |
|
DEPRECATED: interceptFlag |
any |
true |
NoteThis parameter is deprecated and will be removed in a future release. |
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. |
|
interceptFlag |
true |
A flag that indicates a need to compute β0j. |