Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5
Logistic regression algorithm follows the general workflow described in Training and Prediction > Classification > Usage Model
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 θ0 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: |
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:
In addition to classifier output, logistic regression prediction 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, seeAlgorithms.
Result ID |
Result |
---|---|
probabilities |
Numeric table of size: 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:
containing logarithms of classes' probabilities computed when computeClassesLogProbabilities option is enabled. If nClasses = 2, the table contains logarithms of class "1" probabilities. |
If resultsToCompute does not contain computeClassesLabels, the prediction table is NULL.
If resultsToCompute does not contain computeClassesProbabilities, the probabilities table is NULL.
If resultsToCompute does not contain computeClassesLogProbabilities, 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.