h2. Binary evaluation method

We use _sensitivity_ and _specificity_ as statistical measures of the performance of the binary classification test where

_Sensitivity_ = Σ {color:#99cc00}true different{color} / (Σ{color:#99cc00} true different{color} \+ Σ {color:#ff0000}false similar{color})

and

_Specificity_ = Σ{color:#99cc00} true similar{color} / (Σ {color:#99cc00}true similar{color} \+ Σ{color:#ff0000} false different{color})

and the F-measure is calculated on this basis as shown in the table below:

This is one suggested way which is nicely applicable if we test for binary correctness of calculations, i.e. it is applicable for characterisation and QA

We use _sensitivity_ and _specificity_ as statistical measures of the performance of the binary classification test where

_Sensitivity_ = Σ {color:#99cc00}true different{color} / (Σ{color:#99cc00} true different{color} \+ Σ {color:#ff0000}false similar{color})

and

_Specificity_ = Σ{color:#99cc00} true similar{color} / (Σ {color:#99cc00}true similar{color} \+ Σ{color:#ff0000} false different{color})

and the F-measure is calculated on this basis as shown in the table below:

This is one suggested way which is nicely applicable if we test for binary correctness of calculations, i.e. it is applicable for characterisation and QA