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Ravikumar N
Ravikumar N

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๐‚๐ก๐จ๐จ๐ฌ๐ข๐ง๐  ๐‘๐ข๐ ๐ก๐ญ ๐๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ๐š๐ง๐œ๐ž ๐Œ๐ž๐ญ๐ซ๐ข๐œ ๐Ÿ๐จ๐ซ ๐‚๐ฅ๐š๐ฌ๐ฌ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง! ๐Ÿš€

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.

confusion matrix

๐ŸŽฏ ๐€๐œ๐œ๐ฎ๐ซ๐š๐œ๐ฒ:
โ†’ Indicates the proportion of correctly classified instances among all instances.
โ†’ Inadequate for imbalanced datasets as it might be deceptive.

๐Ÿ’ก ๐๐ซ๐ž๐œ๐ข๐ฌ๐ข๐จ๐ง:
โ†’ Quantifies the proportion of true positives among all positive predictions.
โ†’ High Precision is crucial in scenarios where false positives are undesirable.
โ†’ It aids in addressing the query: "Among all the instances predicted as positive, how many are truly positive?"

๐Ÿ“Š ๐‘๐ž๐œ๐š๐ฅ๐ฅ:
โ†’ Computes the proportion of true positives among all actual positives.
โ†’ Also referred to as sensitivity or true positive rate.
โ†’ High Recall is crucial in scenarios where false negatives are undesirable.
โ†’ It aids in answering the question: "Of all the actual positive instances, how many did we accurately identify?"

๐Ÿ“ ๐…1 ๐’๐œ๐จ๐ซ๐ž:
โ†’ Represents the harmonic mean of precision and recall.
โ†’ Incorporates both precision and recall, yielding a unified metric that balances the two.

๐Ÿ” ๐‹๐ž๐ญ'๐ฌ ๐๐ข๐ฌ๐œ๐ฎ๐ฌ๐ฌ:
โ†’ Which evaluation metric do you primarily utilize in your domain?
โ†’ Are there any additional metrics you employ aside from the ones discussed?

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