Author(s): Anmol Tomar
Originally published on Towards AI.
The intuition behind the classification evaluation metrics
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Imagine you are building a fraud detection model to identify the fraudulent transactions done using a credit card. You look into the data and find that the majority of the transactions are non-fraudulent(99%), and only 1% of the transactions are fraudulent. You simply tagged every transaction as non-fraudulent and got an accuracy of 99%, WOW!
But, if you go to the client to deploy such a model, they will call you a FRAUD U+1F601.
The above scenario is an example of an imbalanced classification problem, and the “accuracy” is… Read the full blog for free on Medium.
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Published via Towards AI