On 10/05/2018 12:00 PM, Kevin Markham wrote:
Hello all,
Congratulations on the release of 0.20! My questions are about the
updated classification_report:
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
Here is the simple example shown in the documentation (apologies for
the formatting):
>>> from sklearn.metrics import classification_report
>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]
>>> target_names = ['class 0', 'class 1', 'class 2']
>>> print(classification_report(y_true, y_pred,
target_names=target_names))
precision recall f1-score support
class 0 0.50 1.00 0.67 1
class 1 0.00 0.00 0.00 1
class 2 1.00 0.67 0.80 3
micro avg 0.60 0.60 0.60 5
macro avg 0.50 0.56 0.49 5
weighted avg 0.70 0.60 0.61 5
I understand how macro average and weighted average are calculated. My
questions are in regard to micro average:
1. From this and other examples, it appears to me that "micro average"
is identical to classification accuracy. Is that correct?
2. Is there a reason that micro average is listed three times (under
the precision, recall, and f1-score columns)? From my understanding,
that 0.60 number is being calculated once but is being displayed three
times. The display implies (at least in my mind) that 0.60 is being
calculated from the three precision numbers, and separately calculated
from the three recall numbers, and separately calculated from the
three f1-score numbers, which seems misleading.
3. The documentation explains micro average as "averaging the total
true positives, false negatives and false positives". If my
understanding is correct that micro average is the same as accuracy,
then why are true negatives any less relevant to the calculation?
(Also, I don't mean to be picky, but "true positives" etc. are whole
number counts rather than rates, and so it seems odd to say that you
are arriving at a rate by averaging counts.)
These may be dumb questions arising from my ignorance... my apologies
if so!
I had exactly the same comments and I find the current behavior
confusing, see https://github.com/scikit-learn/scikit-learn/issues/12334
PR welcome!
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