Getting Started Guide

Contents

Batch Processing

Logistic regression algorithm follows the general workflow described in Training and Prediction > Classification > Usage Model

Training

For a description of the input and output, refer to Usage Model: Training and Prediction.
In addition to the parameters of classifier described in Training and Prediction > Classification > Usage Model, the logistic regression batch training algorithm has 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
The computation method used by the logistic regression. The only training method supported so far is the default dense method.
nClasses
Not applicable.
The number of classes. A required parameter.
interceptFlag
True
A flag that indicates a need to compute
θ
0
j
penaltyL1
0
L1 regularization coefficient
penaltyL2
0
L2 regularization coefficient
optimizationSolver
SGD solver
All iterative solvers are available as optimization procedures to use at the training stage:

Prediction

For a description of the input, refer to Usage Model: Training and Prediction.
At the prediction stage logistic regression batch algorithm has 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
The computation method used by logistic regression. The only prediction method supported so far is the default dense method.
nClasses
Not applicable.
The number of classes. A required parameter.
DEPRECATED:
resultsToCompute
USE INSTEAD:
resultsToEvaluate
DEPRECATED:
computeClassesLabels
USE INSTEAD:
classifier::computeClassesLabels
The 64-bit integer flag that specifies which extra characteristics of the logistic regression to compute.
Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:
DEPRECATED
  • computeClassesLabels
    for prediction
  • computeClassesProbabilities
    for probabilities
  • computeClassesLogProbabilities
    for logProbabilities
USE INSTEAD
  • classifier::computeClassLabels
    for prediction
  • classifier::computeClassProbabilities
    for probabilities
  • classifier::computeClassLogProbabilities
    for logProbabilities

Output

Prediction, probabilities, logProbabilities tables are available to compute for logistic regression prediction algorithms. Pass the result ID as a parameter to the methods that access the results of the algorithm. The algorithm calculates the result of type
classifier::prediction::Result
.
For more details, see Algorithms.
The size of the
probabilities
and
logProbabilities
result tables are changed for case
nClasses
= 2: from
n
x 1 to
n
x
nClasses
.
Result ID
Result
probabilities
Numeric table of size:
  • n
    x
    nClasses
containing probabilities of classes computed when
computeClassesProbabilities
option is enabled. If
nClasses
= 2, the table contains probabilities of class "1".
logProbabilities
Numeric table of size:
  • n
    x
    nClasses
containing logarithms of classes' probabilities computed when
computeClassesLogProbabilities
option is enabled. If
nClasses
= 2, the table contains logarithms of class "1" probabilities.
  • If
    resultsToEvaluate
    does not contain
    classifier::computeClassLabels
    , the
    prediction
    table is NULL.
  • If
    resultsToEvaluate
    does not contain
    classifier::computeClassProbabilities
    , the
    probabilities
    table is NULL.
  • If
    resultsToEvaluate
    does not contain
    classifier::computeClassLogProbabilities
    , the
    logProbabilities
    table is NULL.
  • By default, each numeric table of this result is an object of the
    HomogenNumericTable
    class, but you can define the result as an object of any class derived from
    NumericTable
    except for
    PackedSymmetricMatrix
    and
    PackedTriangularMatrix
    .

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