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	double	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.

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.

Examples

C++: svm_multi_class_quality_metric_set_batch.cpp

Java*: SVMMultiClassQualityMetricSetBatchExample.java