One thought on “Visualisation, Metrics for Binary Classification”
First of all, congratulations on writing a Blog! 🙂
Comprehensive Coverage: The article effectively discusses various evaluation metrics for binary classification, such as accuracy, precision, recall, F1 score, and ROC-AUC, providing readers with a solid understanding of these concepts.
Visual Aids: The inclusion of visualizations, such as confusion matrices and ROC curves, enhances comprehension by illustrating how these metrics are derived and interpreted.
Practical Examples: The use of real-world examples to explain the application of these metrics aids in bridging the gap between theory and practice.
First of all, congratulations on writing a Blog! 🙂
Comprehensive Coverage: The article effectively discusses various evaluation metrics for binary classification, such as accuracy, precision, recall, F1 score, and ROC-AUC, providing readers with a solid understanding of these concepts.
Visual Aids: The inclusion of visualizations, such as confusion matrices and ROC curves, enhances comprehension by illustrating how these metrics are derived and interpreted.
Practical Examples: The use of real-world examples to explain the application of these metrics aids in bridging the gap between theory and practice.