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Quality Metrics for Binary Classification Algorithms
For two classes and , given a vector of class labels computed at the prediction stage of the classification algorithm and a vector of expected class labels, the problem is to evaluate the classifier by computing the confusion matrix and connected quality metrics: precision, recall, and so on.
QualityMetricsId for binary classification is confusionMatrix.
Details
Further definitions use the following notations:
true positive |
the number of correctly recognized observations for class |
|
true negative |
the number of correctly recognized observations that do not belong to the class |
|
false positive |
the number of observations that were incorrectly assigned to the class |
|
false negative |
the number of observations that were not recognized as belonging to the class |
The library uses the following quality metrics for binary classifiers:
Quality Metric |
Definition |
---|---|
Accuracy |
|
Precision |
|
Recall |
|
F-score |
|
Specificity |
|
Area under curve (AUC) |
For more details of these metrics, including the evaluation focus, refer to [Sokolova09].
The confusion matrix is defined as follows:
Classified as Class |
Classified as Class |
|
---|---|---|
Actual Class |
tp |
fn |
Actual Class |
fp |
tn |
Batch Processing
Algorithm Input
The quality metric algorithm for binary 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 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 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. |
beta |
1 |
The 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 numeric table with the confusion matrix.
NOTE:
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.
|
binaryMetrics |
Pointer to the numeric table that contains quality metrics, which you can access by an appropriate Binary Metrics ID:
NOTE:
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++ (CPU)
Batch Processing:
Java*
Batch Processing: