🔥 Confusion Matrix — More Important Than Accuracy! | Machine Learning Explained Simply скачать в хорошем качестве

🔥 Confusion Matrix — More Important Than Accuracy! | Machine Learning Explained Simply

Your model shows 95 percent accuracy… but is it actually good? 🤔 Welcome to the Confusion Matrix — where real model evaluation begins. In this video, you will clearly understand: What True Positive, True Negative really mean The difference between False Positive and False Negative Why Accuracy can be misleading When Precision matters more When Recall becomes critical How F1 Score balances everything This topic is extremely important for: Machine Learning interviews Data Science placements AI and ML beginners Anyone building classification models 💡 Quick Question: If a cancer detection model fails to detect cancer when it is actually present, is that False Positive or False Negative? Comment your answer before watching 👇🔥 Like 👍 | Subscribe 🔔 | Share 🚀 Drop the next ML topic you want covered. #confusionmatrix #machinelearning #datascience #precision #recall #f1score #ai #classification #mlinterview #legendcoders