Why Accuracy Isn't Enough: Evaluating Machine Learning Models the Right Way
By: Katrine
When building a machine learning model, accuracy is usually the first number people look at. If a model is 95% accurate, it sounds impressive. But accuracy doesn't always tell the full story.
Imagine you're building a model to detect a rare disease. Out of 1,000 patients, only 10 actually have it. If your model predicts that no one has the disease, it will still be 99% accurate but it completely fails its real purpose.
That's why data scientists use more than just accuracy to evaluate a model.
Precision tells you how many of the positive predictions were actually correct. This is important in situations where false alarms are costly, such as spam detection or fraud detection.
Recall measures how many of the actual positive cases the model successfully found. In healthcare, for example, missing a patient with a serious disease can be much more dangerous than mistakenly flagging a healthy one.
To balance both, we use the F1-score, which combines precision and recall into a single metric. It's especially useful when working with imbalanced datasets where one class is much more common than the other.
Another useful tool is the confusion matrix, which breaks predictions into four categories: true positives, true negatives, false positives, and false negatives. Instead of giving you just one number, it shows exactly where your model is making mistakes.
The truth is, there's no single "best" metric. The right one depends on the problem you're trying to solve. A model for diagnosing cancer should focus on catching as many real cases as possible, while a spam filter might prioritize avoiding false positives.
The next time you see a machine learning model with 99% accuracy, don't assume it's perfect. Ask a better question: What kinds of mistakes is it making? Often, that answer tells you far more than accuracy ever could.