SKLearn Metrics Precision, Recall, F-Score and Support

Precision:

• If it's 1 it means all positives are correct
• It may be that many other positives for this class are wrongly labelled as negative (and categorised in other classes), but that's not measured in this metric
• The lower it is, it means more entries from other categories are being mis-classified here

Recall:

• If it's 1 it means it managed to find all the positive samples
• It may be that it wrongly classified other samples for other categories here, but that's not measured in this metric
• The lower it is, it means more entries from this category are being mis-classified into other categories

F-Score: Some kind of mean between precision and recall

Support: Number of occurrence in each category

Reference: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html

Article About Business Rules Versus Machine Learning

We usually program logic using business rules. However, there are cases where business rules are not clear cut and need continuous iterations to get right. This can be costly and difficult. In these cases, the machine learning approach might help.

Article: https://www.linkedin.com/pulse/data-science-machine-learning-vs-rules-based-karthik-guruswamy/