JuniorTheoryCommon

What does this program print?

Python

from sklearn.metrics import confusion_matrix

y_true = [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]
y_pred = [1, 1, 1, 0, 0, 1, 0, 0, 0, 0]
cm = confusion_matrix(y_true, y_pred)
print(cm.ravel().tolist())

[4, 1, 2, 3]

Expecting [3, 2, 1, 4], the textbook layout with the positive class first: scikit-learn puts class 0 first, so ravel() gives TN, FP, FN, TP.
Expecting the columns to be the true classes: rows are the true classes and columns the predicted ones, so the one false positive sits at cm[0, 1].

Which cell of this matrix holds the false negatives, and how many are there?
How would the output change if labels=[1, 0] were passed to confusion_matrix?

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Back to questions

JuniorTheoryCommon

What does this program print?

Python

from sklearn.metrics import confusion_matrix

y_true = [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]
y_pred = [1, 1, 1, 0, 0, 1, 0, 0, 0, 0]
cm = confusion_matrix(y_true, y_pred)
print(cm.ravel().tolist())

[4, 1, 2, 3]

Expecting [3, 2, 1, 4], the textbook layout with the positive class first: scikit-learn puts class 0 first, so ravel() gives TN, FP, FN, TP.
Expecting the columns to be the true classes: rows are the true classes and columns the predicted ones, so the one false positive sits at cm[0, 1].

Which cell of this matrix holds the false negatives, and how many are there?
How would the output change if labels=[1, 0] were passed to confusion_matrix?

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