Confused b/w Precision and recall

Course Q&A Machine Learning Specialization Advanced Learning Algorithms
3

Hi @Raghav_Mahajan,

these metrics are important to understand:

  • false positives (imagine an anomaly detection is sending an alarm even though everything is normal)
  • false negatives (imagine an anomaly detection is sending no alarm even though there is a big anomaly)

Note that you can improve false positives by worsening false negatives and vice versa. So the overall goal is to keep track of both so that they satisfy the business problem in a reasonable accuracy.

Basically: Precision and recall are representing these cases implicitly also have a similar trade off, as shown in this visualization:

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  • precision is about the relevance of the results provided by the model
  • recall is about the completeness of the results.

They determine the performance of a classification model!

Feel free to take a look at this thread: Anomaly Detection: How to improve? - #2 by Christian_Simonis

Best regards
Christian

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