Python Guide to Precision-Recall Tradeoff

What do you think should we consider only the accuracy score as a benchmark for our classification task? Many beginners in this field have misunderstood; getting good accuracy for classification models means they have built a perfect model which classifies every instance. Well, you can consider only accuracy as a benchmark for regression problems. For better understanding, let's take a famous and general example that every Data Science enthusiast comes through, i.e. Diabetes Prediction. So here, both classes means whether a person has diabetes or not is equally important under different conditions. Say you have trained your model for 200K samples with 180K samples as a negative class, 20K samples as a positive class, and you have achieved accuracy greater than 95% sounds good. Hold on!