Differentiate Precision, Recall, Accuracy, and the F1 Score?

Data Science, AI and ML
#1

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