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Accuracy is just the share of predictions your model got right out of all predictions. Simple, but it can quietly mislead you. 🎯

One number feels reassuring, but accuracy hides which kinds of mistakes you make. A high score can mask a model that fails where it matters most.

The danger appears with imbalanced data, where one class is far more common than the other. Here accuracy stops telling the real story.

Say 95% of emails are not spam. A lazy model that labels everything not spam scores 95% accuracy while catching zero spam.

That always-predict-the-majority trick is the majority-class baseline. If your model barely beats it, your accuracy is hollow.

scikit-learn gives accuracy directly, so you can compare a model against that lazy baseline in one line.

Missing a fraud charge and flagging a safe one are very different costs. Accuracy treats every error as equally cheap, which it rarely is.

A medical screen that misses real disease can score high accuracy yet be dangerous. The cost lives in the errors accuracy quietly averages away.

With balanced classes and equal error costs, accuracy is a perfectly honest, easy-to-read summary of performance.

Before trusting any score, check the class balance. Knowing the class distribution tells you whether accuracy can be believed at all.

Accuracy is a starting point, not a verdict. Real evaluation needs metrics that reveal where and how your model fails.

Quick gut check on the spam example.

Accuracy counts correct predictions, but on imbalanced data it hides failures. Always check class balance and error costs first. ✅