True Positive = output labels that are predicted to be True and they are actually True = 4

True Negative = the output labels that are predicted to be False and they are actually False also = 4

False Positive = the output labels that are predicted to be True but they are actually False = 1

False Negative = output labels that are predicted to be False but they are actually True = 1

Precision in machine learning is defined as:

And recall in machine learning is defined as:

So, in our example, TP = 4, FP = 1, FN = 1

So,

And

How to calculate precision and recall using the sklearn Python library?

We can use the following Python code to calculate the precision and recall of a machine learning model.

from sklearn.metrics import precision_score, recall_score

Ya = [False, False, True, True, True, False, True, False, False, True]
Y = [True, False, True, True, False, False, True, False, False, True]

precision = precision_score(Ya, Y)
recall = recall_score(Ya, Y)

print("Precision: ", precision)
print("Recall: ", recall)

Please note that here Ya is the actual or expected output. And Y is the predicted or calculated output. The output of the above program will be:

Precision:  0.8
Recall:  0.8