Balancing Precision and Recall: Navigating Trade-offs in Model Evaluation for Business Success

Enabling businesses to take advantage of Data is extremely helpful in building desirable experience for consumers and communities. Building Data based products do need to use Machine Learning models. One of key basic measure to evaluate models is review them through?Recall and Precision. And it usually creates dilemma how to prioritise one over another. How much or less is good enough.?

It’s extremely important for business to identify what’s objective of feature who and how this will help.?

Lets take an example and dwell in to understanding what parameter may make us meet what objectives.

e.g e-commerce scenario :?Suggest new products to customer based on preference or activity in?

Objectives : As experience we would like to be generally correct. In ML terms we would be OK having few false positives means we identified something customer may like but they may not. Scenario like this helps in exposing new dimensions in future such new products , services never offered or now made available. This should prioritise on Recall.

If you are building products such as recommendation, social, experience where mistake will not bring extraordinary setbacks. This will be broader dimension to explore, new experiments also chance of new surprises.?

Autonomous Driving : System to make decision on real time driving condition and drive vehicle.

Objective : Safe drive?,What will be expected to ensure no accidents, no violation. Which will mean we would like to zero on false positive. This would ideally focus and prioritise on Precision.

If you are building products in high stakes domain such as medical , astronomy, space, autonomy where risk appetite is very low and likely want to be very conservative and give decision which are most confidence on.

While it looks simpler to make decision keeping entire use case in isolation and often real world scenario do push for balance b/w both precision and recall. Go for f1-score.

F1 Score = 2 X (Precision X Recall) ÷ (Precision + Recall)

F1 score is a ML evaluation metric that measures a model’s accuracy. It combines the precision and recall scores of a model.