Precision and Recall if not binary I have to calculate precision and recall for a university project to measure the quality of the classification output (with sklearn).
Say this would be my results:

y_true = [0, 1, 2, 1, 1]
y_pred = [0, 2, 1, 2, 1]

confusion matrix:
[1 0 0]
[0 1 2]
[0 1 0]   

I have read about it and the definition makes sense for me in a binary setting but with 3 labels I find it hard to interpret precision/recall.
If I use sklearn.metrics.precision/recall_score it gives me 0.4 for both (average = micro)

Now for the precision this makes somewhat sense because 2 out of 5 are correctly classified. But I am having problems interpreting the 0.4 result for recall.
sklearn.metrics.classification_report provides precision and recall for all classes along with F-score and support. It might prove to be helpful in your case of 3 classes.