An explanation into the importance of Precision and Recall in Data Science
A common question in Data Science interviews is ‘How would you measure the performance of a classification model when 99% of your data belongs to one class?’
This is a straightforward question, yet many people stumble and don’t know how to respond.
In this article we will explore the answer to the above question by discussing Recall, Precision and the harmonic mean of the two, the F1 score.
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Lets begin with explaining why accuracy may not be a good measure. If 99% of your dataset belongs to one class, then by simple guessing, any model could achieve an accuracy score of 99%!
The model will simply learn really well to predict that single class, hence achieve an accuracy score which is equal to the distribution of classes.
Even without a Machine Learning model, I can purely guess what class a sample belongs to and achieve an accuracy of 99%!
Therefore, for this particular problem accuracy is not a good metric to measure model performance. Hence, we have to seek alternative measurements.
Note: Sometimes accuracy is a good measure, but it all depends on the context of the work.
Before we continue, it is important to understand the Confusion Matrix. This breaks down your results into correct and incorrect classifications for both classes and is more informative in measuring performance than just accuracy.
