Beyond the Buzz: Understanding Classification Model Performance
When you first start building machine learning models, it's natural to focus on one simple question: "How often is it right?" You might calculate accuracy by dividing the correct predictions by the total. But what if "being right" isn't always enough? Imagine a medical test that predicts a rare genetic defect. If only 10 out of 100,000 people have the defect, a test that always says "no defect" will be 99.99% accurate! Sounds great, right? But it missed every single person who actually had the defect. Clearly, raw accuracy doesn't tell the whole story. Just like a good teacher assesses students not just on right/wrong answers but on their understanding and ability to generalize, we need to evaluate our machine learning models with more nuance. Why Simple Accuracy Can Be Deceiving The problem above is a classic example of class imbalance , where one outcome (no defect) is far more common than the other (defect). In such cases, a model can look ...