I use the "classification_report" from
from sklearn.metrics import classification_report in order to evaluate the imbalanced binary classification
Classification Report : precision recall f1-score support 0 1.00 1.00 1.00 28432 1 0.02 0.02 0.02 49 accuracy 1.00 28481 macro avg 0.51 0.51 0.51 28481 weighted avg 1.00 1.00 1.00 28481
I do not understand clearly what is the meaning of macro avg and weighted average? and how we can clarify the best solution based on how close their amount to one!
I have read it: macro average (averaging the unweighted mean per label), weighted average (averaging the support-weighted mean per label)
but I still have a problem in understanding how good is result based on how close these amount to 1? How I can explain it?