The Day Accuracy Lied
The Day Accuracy Lied
Imagine a doctor says, "Our AI is 99% accurate. " Sounds incredible, doesn't it? Would you trust it with your life? Not so fast. Now imagine this hospital tests 10,000 patients. Only 100 actually have cancer. The AI simply predicts... "Everyone is healthy. " It gets 9,900 predictions correct. That's 99% accuracy. But it completely missed every single cancer patient. Technically... The AI is 99% accurate. Practically... It's useless. This is the biggest lesson in Machine Learning. Being right most of the time doesn't mean you're making the right decisions. Some mistakes cost almost nothing. Others can cost money... jobs... or even lives. So data scientists don't judge an AI using just one number. Instead, they ask deeper questions. When the AI says "Yes," can we trust it? Did it miss anyone important? How many false alarms did it raise? Which mistakes are more dangerous? Each question has its own measurement. These measurements are called evaluation metrics. Throughout this playlist, we'll learn how AI is actually judged—not just by how often it's right, but by how it's right, where it fails, and whether those failures matter. Because in Artificial Intelligence... A wrong answer isn't always equally wrong. And understanding that difference is what separates using AI from engineering it.