Every Classification Metric is Just Four Counts

A 97% accurate spam filter that catches exactly zero spam. That's the trap a single accuracy number sets for you, and it's why the confusion matrix exists. Stop hiding behind one number, write down what actually happened, and four counts fall out: true positives, true negatives, false positives, false negatives. This video builds those four counts on a worked example, then shows that every classification metric you've ever heard of, accuracy, precision, recall, F1, is just a ratio between them. We walk through where accuracy breaks under class imbalance, why precision is the metric you care about when false alarms are expensive, why recall is the metric you care about when missing a positive hurts, and how the harmonic mean (F1) refuses to let you game one at the expense of the other. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Cracking ML Interviews: Precision, Recall and F1-Score (Question 13): https://youtu.be/XNYCstxJOy4 Cracking ML Interviews: ROC and AUC (Question 12): https://youtu.be/wOSeWUzfhV8 ROC Curve - Explained: https://youtu.be/lV4VCUC5gS4 Logistic Regression - Explained: https://youtu.be/FiDWEnve9go Cross-Entropy - Explained: https://youtu.be/Fv98vtitmiA Cross-Validation Explained: https://youtu.be/hEtWQcIL3NI Why We Don't Use the Mean Squared Error (MSE) Loss in Classification: https://youtu.be/bNwI3IUOKyg Overfitting vs Underfitting - Explained: https://youtu.be/B9rhzg6_LLw *Contents* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - A Trap in One Number 00:37 - Four Outcomes 01:24 - Accuracy and Where It Breaks 02:12 - Precision 02:55 - Recall 03:34 - The Trade *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 X: @datamlistic https://x.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic 👔 Linkedin: https://www.linkedin.com/company/datamlistic *Channel Support* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the