Classification Metrics Explained — Precision, Recall, F1 & AUC скачать в хорошем качестве

Classification Metrics Explained — Precision, Recall, F1 & AUC

Accuracy alone doesn’t tell the full story when evaluating classification models. In this video, we explain classification evaluation metrics from first principles. What precision, recall, F1-score, and AUC actually measure, how they differ, and when each metric should be used. You’ll learn: • Why accuracy can be misleading • What precision really measures • What recall captures • How F1 balances precision and recall • What AUC represents and why it matters • How to choose the right metric for your problem This video is part of Notebook Learning, a channel focused on clear, visual explanations of complex topics in Data Science, Machine Learning, AI, and NLP. Whether you’re a beginner, student, or developer, this video will help you evaluate classification models correctly in just a few minutes. 📘 New five-minute explainer videos coming regularly. #ClassificationMetrics #Precision #Recall #F1Score #AUC #MachineLearning #DataScience #NotebookLearning