[scikit-learn] Micro average in classification report

Kevin Markham kevin at dataschool.io
Fri Oct 5 12:00:20 EDT 2018

```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,

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!

As well, I don't mean for my questions to criticize the excellent work that
has been done by all of the scikit-learn contributors - I deeply appreciate
your work! Rather, I'm planning to create a video series explaining some of
the new features in 0.20, and I want to make sure that I'm accurately
explaining these new features.

Thanks very much!
Kevin

--
Kevin Markham
Founder, Data School
https://www.dataschool.io