The most common use of logistic regression models is in binary classification problems. So, let’s hear from Prof. Dinesh what a binary classification problem is.
So, now you know what a binary classification problem is. In the next video, Prof. Dinesh explains it with a specific example. Let’s see what that is.
You can download the data set for the diabetes example from below.
Now, recall the graph of the diabetes example. Suppose there is another person, with a blood sugar level of 195, and you do not know whether that person has diabetes or not. What would you do then? Would you classify him/her as a diabetic or as a non-diabetic?
Now, based on the boundary, you may be tempted to declare this person a diabetic, but can you really do that? This person’s sugar level (195 mg/dL) is very close to the threshold (200 mg/dL), below which people are declared as non-diabetic. It is, therefore, quite possible that this person was just a non-diabetic with a slightly high blood sugar level. After all, the data does have people with slightly high sugar levels (220 mg/dL), who are not diabetics.
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