Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 2

Details

Further definitions use the following notations:

tp i (true positive)

the number of correctly recognized observations for class C i

tn i (true negative)

the number of correctly recognized observations that do not belong to the class C i

fp i (false positive)

the number of observations that were incorrectly assigned to the class C i

fn i (false negative)

the number of observations that were not recognized as belonging to the class C i

The library uses the following quality metrics for multi-class classifiers:

Quality Metric

Definition

Average accuracy

Error rate

Micro precision (Precision μ )

Micro recall (Recall μ )

Micro F-score (F-score μ )

Macro precision (Precision M)

Macro recall (Recall M)

Macro F-score (F-score M)

For more details of these metrics, including the evaluation focus, refer to [Sokolova09].

The following is the confusion matrix:

Classified as Class C 1

...

Classified as Class C i

...

Classified as Class C l

Actual Class C 1

c 11

...

c 1i

...

c 1l

...

...

...

...

...

...

Actual Class C i

c i1

...

c ii

...

c il

...

...

...

...

...

...

Actual Class C l

c l1

...

c li

...

c ll

The positives and negatives are defined through elements of the confusion matrix as follows: