Precision is the ratio of correctly predicted positive observation and total predicted positive observation. It shows how precise our model is.
* Precision = TP/TP+FP
Recall is the ratio of the correct predicted positive observation and the total observation in the class.
* Recall = TP/TP+FN
F1-Score is the weighted average of recall and precision.
* F1-Score = 2*(Recall * Precision) / (Recall + Precision)
Accuracy is the ratio of correctly predicted positive observations to the total positive observations.
* Accuracy = TP+TN/TP+TN+FP+FN
