Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
You can use linear or ridge regression in the online processing mode only at the training stage.
This computation mode assumes that the data arrives in blocks i = 1, 2, 3, … nblocks.
Linear or ridge regression training in the online processing mode follows the general workflow described in Usage Model: Training and Prediction.
Linear or ridge regression training in the online processing mode accepts 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.
Input ID |
Input |
|
---|---|---|
data |
Pointer to the ni x p numeric table that represents the current, i-th, data block. This table can be an object of any class derived from NumericTable. |
|
dependentVariables |
Pointer to the ni x k numeric table with responses associated with the current, i-th, data block. This table can be an object of any class derived from NumericTable. |
The following table lists parameters of linear and ridge regressions at the training stage in the online processing mode. Some of these parameters or their values are specific to a linear or ridge regression algorithm.
For a description of the output, refer to Usage Model: Training and Prediction.
C++:
Java*:
Python*: