# What is precision recall tradeoff ?

Tradeoff means increasing one parameter leads to decreasing of the other. Let us explain this in context to binary classification and first define what is precision and recall. Let us call one class as positive and other as negative. Then,

1. TP represents the true positives, which is the number of positive predictions which are actually positive.
2. FP represents the false positives, which is the number of negative predictions incorrectly classified as a positive ,i.e. they were identified as positives though they were from a negative class.
3. TN represents the true negatives, which is the number of negative predictions correctly classified  as negative.
4. FN represents the false negatives which is the number of positive instances incorrectly identified into the negative class, i.e. they were identified as negative but in reality they were  from the positive class.

Precision is the fraction of correct positives among the total predicted positives. It is also called the accuracy of positive predictions. Recall is the fraction of correct positives among the total positives in the dataset. It is indicating how many total positives of the actual dataset were covered(classified correctly) while doing prediction. 