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:

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

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

So,

And

Therefore,

How to calculate the F1 score using the sklearn library in Python?

We can use the following Python code to calculate the F1 score of a machine learning model.

from sklearn.metrics import precision_score, recall_score, f1_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)
f1 = f1_score(Ya, Y)

print("Precision: ", precision)
print("Recall: ", recall)
print("F1 Score: ", round(f1, 2))

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
F1 Score:  0.8