Choosing right performance metric for classification! 🚀

Feeling bewildered about which metrics to employ for evaluating your binary classification model? Let's navigate through and ascertain the optimal way to assess the classification model.

🎯 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: → Indicates the proportion of correctly classified instances among all instances. → Inadequate for imbalanced datasets as it might be deceptive.

💡 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧: → Quantifies the proportion of true positives among all positive predictions. → High Precision is crucial in scenarios where false positives are undesirable. → It aids in addressing the query: "Among all the instances predicted as positive, how many are truly positive?"

📊 𝐑𝐞𝐜𝐚𝐥𝐥: → Computes the proportion of true positives among all actual positives. → Also referred to as sensitivity or true positive rate. → High Recall is crucial in scenarios where false negatives are undesirable. → It aids in answering the question: "Of all the actual positive instances, how many did we accurately identify?"

📐 𝐅1 𝐒𝐜𝐨𝐫𝐞: → Represents the harmonic mean of precision and recall. → Incorporates both precision and recall, yielding a unified metric that balances the two.

🔍 𝐋𝐞𝐭'𝐬 𝐝𝐢𝐬𝐜𝐮𝐬𝐬: → Which evaluation metric do you primarily utilize in your domain? → Are there any additional metrics you employ aside from the ones discussed?

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