Precision = TP/TP+FP
Precision means of all the points, the model predicted to be positive, what percentage are actually positive.
Recall = TPR(True Positive Rate) TP/P
TPR = TP/TP+FN
Recall means of all the points that actually belonged to class 1, how many of them have been predicted to be positive.
Precision can lie between 0-1, similarily Recall can lie between 0-1.
For a good model, we want precision to be high and recall to be high.
F1 Score = 2* Precision*Recall/(Precision + Recall)
F1 Score is used in a lot of kaggle competitions.