This article outlines precision recall curve and how it is used in real-world data science application. It includes explanation of how it is different from ROC curve. It also highlights limitation of ROC curve and how it can be solved via area under precision-recall curve. This article also covers implementation of area under precision recall curve in Python, R and SAS.
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Table of Contents
What is Precision Recall Curve?
Before getting into technical details, we first need to understand precision and recall terms in layman's term. It is essential to understand the concepts in simple words so that you can recall it for future work when it is required. Both Precision and Recall are important metrics to check the performance of binary classification model.Precision
Precision is also called Positive Predictive Value. Suppose you are building a customer attrition model which has objective to identify customers who are likely to close relationship with the company. The use of this model is to prevent attrition and boost customer profitability. It's a binary classification problem in which dependent variable is binary in nature. It has only two values either 0 or 1. 1 refers to customers who left us. 0 refers to active customers who are still with us. In this case, precision is the proportion of customers our predictive model call as attritors actually left us (attrited).Let's understand it by confusion matrix
Precision = True Positive / (True Positive + False Positive)
- True Positive : Number of customers who actually attrited whom we correctly predicted as attritors.
- False Positive : Number of customers who actually did not attrite whom we incorrectly predicted as attritors.