JuniorTheoryCommon

What does this program print?

Python

from sklearn.metrics import precision_score, recall_score

y_true = [1, 1, 1, 1, 0, 0, 0, 0]
y_pred = [1, 1, 0, 0, 1, 0, 0, 0]
p = precision_score(y_true, y_pred)
r = recall_score(y_true, y_pred)
print(round(p, 2), round(r, 2))

0.67 0.5

Expecting 0.5 0.67, with the denominators swapped: precision divides TP by the predicted positives (2 of 3), recall by the real positives (2 of 4).
Expecting 0.62 for both: that is the share of correct answers, 5 of 8, which counts both classes; precision and recall look only at class 1.

How would both values change if the model also predicted 1 for the fourth sample?
Which of the two metrics matters more when a missed positive is very costly?

Back to questions Open topic in roadmap →Answer it yourself — Quiz

Back to questions

JuniorTheoryCommon

What does this program print?

Python

from sklearn.metrics import precision_score, recall_score

y_true = [1, 1, 1, 1, 0, 0, 0, 0]
y_pred = [1, 1, 0, 0, 1, 0, 0, 0]
p = precision_score(y_true, y_pred)
r = recall_score(y_true, y_pred)
print(round(p, 2), round(r, 2))

0.67 0.5

Expecting 0.5 0.67, with the denominators swapped: precision divides TP by the predicted positives (2 of 3), recall by the real positives (2 of 4).
Expecting 0.62 for both: that is the share of correct answers, 5 of 8, which counts both classes; precision and recall look only at class 1.

How would both values change if the model also predicted 1 for the fourth sample?
Which of the two metrics matters more when a missed positive is very costly?

Back to questions Open topic in roadmap →Answer it yourself — Quiz