📉 Why ‘Accuracy’ Is Wrecking Your Project

Your model reports 97% accuracy.
Everyone's impressed — until the results go live.
That’s when reality hits: it's flagging nothing useful.

Why?

Because accuracy is misleading, especially with imbalanced data.

⚠️ Why Accuracy Fails

Say you’re predicting fraud. Only 2% of cases are fraudulent.
If your model just predicts “not fraud” every time, it’ll still be 98% accurate.

✅ High accuracy
❌ Zero value

This is why you need better metrics.

✅ The Metrics That Actually Matter

Let’s break down what to use instead — and when.

1. Precision

What it answers:
When the model says “positive,” how often is it right?

Useful when false positives are costly
(e.g., diagnosing a rare disease, flagging legitimate users as frauds)

2. Recall

What it answers:
How many actual positives did we catch?

Use when missing positives is dangerous
(e.g., catching fraud, identifying cancer)

3. F1 Score

What it does:
Balances precision and recall into one number.

F1 = 2 * (precision * recall) / (precision + recall)

Best when you care about both false positives and false negatives.

4. ROC-AUC

What it shows:
Model’s ability to distinguish between classes across all thresholds.

Use it to compare models, especially when class imbalance is high.

🔁 TL;DR: Choose the Right Metric

📊 Poll

What metric do you use most for model evaluation?
Cast your vote here — results in next issue.