Accuracy is what many beginners look for when building an ML model. Imagine you have a model that detects skin diseases. Your test dataset has 10,000 patients, out of which only 100 have the disease, while the remaining are healthy. The model ends up predicting everyone as healthy. That would give a 99% accuracy, which sounds impressive, but it failed to identify a single sick patient.Now, think about deploying it in the real world while blindly trusting its accuracy. That would be disastrous, as it failed to identify even a single sick person during testing. This is why models can fail in the real world even though they gave impressive accuracy during the test phase. To actually understand the model’s behaviour in real life, we need to look at metrics beyond accuracy.