I have performed GaussianNB classification using sklearn. I tried to calculate the metrics using the following code:
print accuracy_score(y_test, y_pred)
print precision_score(y_test, y_pred)
Accuracy score is working correctly but precision score calculation is showing error as:
ValueError: Target is multiclass but average='binary'. Please choose another average setting.
As target is multiclass, can i have the metric scores of precision, recall etc.?
The function call precision_score(y_test, y_pred) is equivalent to precision_score(y_test, y_pred, pos_label=1, average='binary').
The documentation (http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) tells us:
'binary':
Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.
So the problem is that your labels are not binary, but probably one-hot encoded. Fortunately, there are other options which should work with your data:
precision_score(y_test, y_pred, average=None) will return the precision scores for each class, while
precision_score(y_test, y_pred, average='micro') will return the total ratio
of tp/(tp + fp)
The pos_label argument will be ignored if you choose another average option than binary.
