Now the patient will get treatment for his actual illness instead of taking Covid treatment. This is because the case was actually negative and was also classified as negative which is the right thing to do. Let’s say the patient was not suffering from Covid and the doctor also gave him a clean chit. This is an unnecessary inconvenience for him and others as he will get unwanted treatment and quarantine. Now, the patient will end up getting admitted to the hospital or home and will be given treatment for Covid. This is because the case was actually negative but was falsely classified as positive. Let’s say the patient was not suffering from Covid and he was only showing symptoms of seasonal flu but the doctor diagnosed him with Covid. Now, the patient can be given appropriate treatment which means, the decision made by the doctor will have a positive effect on the patient and society. This is because the case is positive in real and at the same time the case was classified correctly. Let’s say the patient was actually suffering from Covid and on doing the required assessment, the doctor classified him as a Covid patient. Hence, it is tricky for the doctor to do the right diagnosis. These are symptoms that can occur during any seasonal changes too. Since it’s the season of Covid, let’s assume that he went with fever, cough, throat ache, and cold. Use case: Let’s take an example of a patient who has gone to a doctor with certain symptoms. Terms used in defining a confusion matrix are TP, TN, FP, and FN. What are the Performance Evaluation Measures for Classification Models?Ĭonfusion Matrix usually causes a lot of confusion even in those who are using them regularly. Once the jargons are clear, we can use them in the most appropriate manner and we can knit the perfect classification model. There are so many performance evaluation measures when it comes to selecting a classification model that our brain can get tangled just like a thread ball during knitting! In this blog, my intention is to declutter and organize the several jargon used in classification problems from a binary classification point of view.
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