The indices used in evaluating prediction results of this component are described below.

Evaluation Index

Type

Description

true_positive

int

Number of samples determined as positive correctly (TP).

false_positive

int

Number of samples determined as positive incorrectly (FP).

true_negative

int

Number of samples determined as negative correctly (TN).

false_negative

int

Number of samples determined as negative incorrectly (FN).

accuracy

float

Proportion of true results in the population as shown below:

\(\frac{\mbox{TP} + \mbox{TN}}{\mbox{TP} + \mbox{FP} + \mbox{TN} + \mbox{FN}}\)

classification_error

float

Proportion of false results in the population as shown below:

\(\frac{\mbox{FP} + \mbox{FN}}{\mbox{TP} + \mbox{FP} + \mbox{TN} + \mbox{FN}} = 1 - \mbox{accuracy}\)

precision

float

Proportion of the true_positive against all samples determined as positive as shown below:

\(\frac{\mbox{TP}}{\mbox{TP} + \mbox{FP}}\)

recall

float

Proportion of the true_positive against all the actual positive samples as shown below:

\(\frac{\mbox{TP}}{\mbox{TP} + \mbox{FN}}\)

specificity

float

Proportion of the true_negative against all the actual negative samples as shown below:

\(\frac{\mbox{TN}}{\mbox{TN} + \mbox{FP}}\)

false_positive_rate

float

Proportion of the false_positive against all the actual negative samples as shown below:

\(\frac{\mbox{FP}}{\mbox{TN} + \mbox{FP}} = 1 - \mbox{specificity}\)

false_negative_rate

float

Proportion of the false_negative against all the actual positive samples as shown below:

\(\frac{\mbox{FN}}{\mbox{TP} + \mbox{FN}} = 1 - \mbox{recall}\)

f_measure

float

Harmonic mean of precision and recall as shown below:

\(\frac{2 \times \mbox{precision} \times \mbox{recall}}{\mbox{precision} + \mbox{recall}}\)

auc

float

Area under ROC (Receiver Operating Characteristic) curve.

area_under_precision_recall

float

Area under PR (Precision-Recall) curve.