What are precision and recall in machine learning?
In a classification problem, precision and recall are two metrics using which we can measure the performance of the machine learning model.
We know that:
True Positives (TP): True positives are those output labels that are predicted to be True and they are actually True.
True Negatives (TN): True negatives are the output labels that are predicted to be False and they are actually False also.
False Positives (FP): False positives are the output labels that are predicted to be True but are actually False.
False Negatives (FN): False negatives are those output labels that are predicted to be False but are actually True.
Let’s say the expected or actual output for a classification problem is:
Ya = [False, False, True, True, True, False, True, False, False, True]
And the calculated or predicted output is:
Y = [True, False, True, True, False, False, True, False, False, True]
So, in this example, the total number of: …






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