Developer Guide

Contents

Usage Model: Training and Prediction

A typical workflow for regression methods includes training and prediction, as explained below.

Algorithm-Specific Parameters

The parameters used by regression algorithms at each stage depend on a specific algorithm. For a list of these parameters, refer to the description of an appropriate regression algorithm.

Training Stage

Regression Training Workflow
At the training stage, regression algorithms accept 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
n
x
p
numeric table with the training data set. This table can be an object of any class derived from
NumericTable
.
weights
Weights of the observations in the training data set. Optional argument.
dependentVariables
Pointer to the
n
x
k
numeric table with responses (
k
dependent variables). This table can be an object of any class derived from
NumericTable
except
PackedSymmetricMatrix
and
PackedTriangularMatrix
.
At the training stage, regression algorithms calculate 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 regression model being trained. The result can only be an object of the
Model
class.

Prediction Stage

Regression Prediction Workflow
At the prediction stage, regression algorithms accept 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
n
x
p
numeric table with the working data set. This table can be an object of any class derived from
NumericTable
.
model
Pointer to the trained regression model. This input can only be an object of the
Model
class.
At the prediction stage, regression algorithms calculate 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
prediction
Pointer to the
n
x
k
numeric table with responses (
k
dependent variables). By default, this table is an object of the
HomogenNumericTable
class, but you can define it as an object of any class derived from
NumericTable
except
PackedSymmetricMatrix
and
PackedTriangularMatrix
.

Accessing API References

Intel® DAAL provides application programming interfaces for C++, Java*, and Python* languages. Visit Intel® Data Analytics Acceleration Library API Reference to download API References for C++, Java*, and Python*. API Reference for C++ is also available online on IDZ, see C++ API Reference for Intel® Data Analytics Acceleration Library
.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804