I was looking at the classification report from sklearn. I am wondering, why did they omit a potential third row with precision and recall values for both classes together? Why were they split apart, and what's the disadvantage to considering these metrics with both classes combined?
"Precision and recall values for both classes together" is contained in the classification_report as macro averages and weighted averages for precision, recall, and f1-score.
Compare the column in classification_report to the values computed when calling precision_score(y_true, y_pred):
from sklearn.metrics import classification_report
from sklearn.metrics import precision_score
y_true = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2]
y_pred = [0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 0]
print(classification_report(y_true, y_pred))
print(round(precision_score(y_true, y_pred, average='macro'), 2))
print(round(precision_score(y_true, y_pred, average='weighted'), 2))
Running this results in the following. Notice that macro-averaged precision is 0.64 and weighted-average precision is 0.67, and both those are listed in the bottom rows of the table:
precision recall f1-score support
0 0.43 0.60 0.50 5
1 0.50 0.57 0.53 7
2 1.00 0.57 0.73 7
accuracy 0.58 19
macro avg 0.64 0.58 0.59 19
weighted avg 0.67 0.58 0.60 19
0.64
0.67