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.
๐ฏ ๐๐๐๐ฎ๐ซ๐๐๐ฒ:
โ Indicates the proportion of correctly classified instances among all instances.
โ Inadequate for imbalanced datasets as it might be deceptive.
๐ก ๐๐ซ๐๐๐ข๐ฌ๐ข๐จ๐ง:
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?"
๐ ๐๐๐๐๐ฅ๐ฅ:
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?"
๐ ๐
1 ๐๐๐จ๐ซ๐:
โ Represents the harmonic mean of precision and recall.
โ Incorporates both precision and recall, yielding a unified metric that balances the two.
๐ ๐๐๐ญ'๐ฌ ๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ:
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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