Developer Guide

Contents

Batch Processing

Algorithm Input

The quality metric algorithm for multi-class classifiers 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
predictedLabels
Pointer to the
n
x 1 numeric table that contains labels computed at the prediction stage of the classification algorithm. This input can be an object of any class derived from
NumericTable
except
PackedSymmetricMatrix
,
PackedTriangularMatrix
, and
CSRNumericTable
.
groundTruthLabels
Pointer to the
n
x 1 numeric table that contains expected labels. This input can be an object of any class derived from
NumericTable
except
PackedSymmetricMatrix
,
PackedTriangularMatrix
, and
CSRNumericTable
.

Algorithm Parameters

The quality metric 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
Performance-oriented computation method, the only method supported by the algorithm.
nClasses
0
The number of classes (
l
).
useDefaultMetrics
true
A flag that defines a need to compute the default metrics provided by the library.
beta
1
The
β
parameter of the F-score quality metric provided by the library.

Algorithm Output

The quality metric algorithm 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
confusionMatrix
Pointer to the
nClasses
x
nClasses
numeric table with the confusion matrix. By default, 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
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
multiClassMetrics
Pointer to the 1 x 8 numeric table that contains quality metrics, which you can access by an appropriate Multi-class Metrics ID:
  • averageAccuracy
    - average accuracy
  • errorRate
    - error rate
  • microPrecision
    - micro precision
  • microRecall
    - micro recall
  • microFscore
    - micro F-score
  • macroPrecision
    - macro precision
  • macroRecall
    - macro recall
  • macroFscore
    - macro F-score
By default, 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
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

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