In this lecture, we dive into advanced evaluation metrics for Machine Learning models. We will cover Precision, Recall, and F1-Score, which are essential for understanding model performance beyond accuracy. You'll also learn about multi-class classification techniques, including the One-vs-One and One-vs-All methods. Additionally, we'll explore Macro-Averaging and Micro-Averaging to evaluate models across different classes, and wrap up with Cohen’s Kappa, a metric that measures agreement between predicted and actual labels. By the end of this lecture, you'll have a solid understanding of these metrics to improve your model evaluation.

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