Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 2
You can use linear or ridge regression in the distributed processing mode only at the training stage.
This computation mode assumes that the data set is split in nblocks blocks across computation nodes.
Algorithm Parameters
The following table lists parameters of linear and ridge regressions at the training stage in the distributed processing mode. Some of these parameters or their values are specific to a linear or ridge regression algorithm.
Use the two-step computation schema for linear or ridge regression training in the distributed processing mode, as illustrated below:
In this step, linear or ridge regression training 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 i-th data block on the local node. 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 i-th data block. This table can be an object of any class derived from NumericTable. |
In this step, linear or ridge regression training calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.
Result ID |
Result |
|
---|---|---|
partialModel |
Pointer to the partial linear regression model that corresponds to the i-th data block. The result can only be an object of the Model class. |
Step 2 - on Master Node
In this step, linear or ridge regression training 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 |
|
---|---|---|
partialModels |
A collection of partial models computed on local nodes in Step 1. The collection contains objects of the Model class. |
In this step, linear or ridge regression training calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.
Result ID |
Result |
|
---|---|---|
model |
Pointer to the linear or ridge regression model being trained. The result can only be an object of the Model class. |