Table 6.

Performance evaluation metrics

S. no	Performance measures	Description	Mathematical Expression
1	Accuracy	Measure how often the algorithm classifies a data point correctly.	Accuracy $=\frac{\text{T}{}_{\text{P}}+\text{T}{}_{\text{N}}}{{\text{T}}_{\text{P}}+{\text{T}}_{\text{N}}+\text{F}{}_{\text{P}}+{\text{F}}_{\text{N}}}$
2	Specificity	Measure the model’s capability to determine true negatives of each available class.	Specificity $=\frac{\text{T}{}_{\text{N}}}{\text{T}{}_{\text{N}}+{\text{F}}_{\text{P}}}$
3	Recall	Measure the model’s capability to determine true positives of each available class.	Recall $=\frac{\text{T}{}_{\text{P}}}{\text{T}{}_{\text{P}}+\text{F}{}_{\text{N}}}$
4	Precision	Defines how close measurements are to each other.	Precision $=\frac{\text{T}{}_{\text{P}}}{\text{T}{}_{\text{P}}+\text{F}{}_{\text{P}}}$
5	F-score	Evaluates the harmonic mean of precision and recall	F-score $=2\times \frac{\text{Precision}\times \text{Recall}}{\text{Precision}+\text{Recall}}$