CODEWITHSUNDEEP
pythonmachine-learningscikit-learnclassificationsvm
Crista23
Crista23

Reputation: 3243

Scikit classification report - change the format of displayed results

Scikit classification report would show precision and recall scores with two digits only. Is it possible to make it display 4 digits after the dot, I mean instead of 0.67 to show 0.6783?

 from sklearn.metrics import classification_report
 print classification_report(testLabels, p, labels=list(set(testLabels)), target_names=['POSITIVE', 'NEGATIVE', 'NEUTRAL'])
                     precision    recall  f1-score   support

         POSITIVE       1.00      0.82      0.90     41887
         NEGATIVE       0.65      0.86      0.74     19989
         NEUTRAL        0.62      0.67      0.64     10578

Also, should I worry about a precision score of 1.00? Thanks!

Upvotes: 21

Views: 24885

Answers (2)

alfa
alfa

Reputation: 3088

No, it is not possible to display more digits with classification_report. The format string is hardcoded, see here.

edit: there is an update, see CentAu's answer

Upvotes: 5

CentAu
CentAu

Reputation: 11160

I just came across this old question. It is indeed possible to have more precision points in classification_report. You just need to pass in a digits argument.

classification_report(y_true, y_pred, target_names=target_names, digits=4)

From the documentation:

digits : int Number of digits for formatting output floating point values

Demonstration:

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))

Output:

       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

avg / total       0.70      0.60      0.61         5

With 4 digits:

print(classification_report(y_true, y_pred, target_names=target_names, digits=4))

Output:

             precision    recall  f1-score   support

    class 0     0.5000    1.0000    0.6667         1
    class 1     0.0000    0.0000    0.0000         1
    class 2     1.0000    0.6667    0.8000         3

avg / total     0.7000    0.6000    0.6133         5

Upvotes: 54

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