Contents

# 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
λ
j
for
j
= 1, …,
k
.
• size = 1 x 1: the value of the ridge parameter for each dependent variable
λ
1
= ... =
λ
k
.
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
β
0
j
.

## 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.

#### Product and Performance Information

1

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Notice revision #20110804