When we care more that there should be no false negatives, as far as possible… ie. higher recall (video is suitable for kid or not), we should use (receiver operating characteristic) ROC (area under the curve) AUC and try to maximize it.
Scikit-Learn provides a function to compute this directly:
from sklearn.metrics import roc_auc_score roc_auc_score(y_train_5, y_scores)
Similarly, when we care more about the false positives than the false negatives, example shop lifting case then what should we do?
Should we just try to maximize the recall ignoring the precision or is there a better metric?
I had read somewhere "Use the precision vs recall curve and get that max" but not very sure what that means... Can you please explain the same?
If there a direct function for this metric in sci-kit learn, like above for the first case, please do let me know.