Good day I would like to be corrected with regards to the below issue which would be a good definition of defining a problem type out of the below scenario.
Predicting whether or not a patient is likely to be readmitted on their initial discharge date is an example of the following problem type.
I would take it that it's a Regression problem type.
Could you please assist.
Solved! Go to Solution.
@Siya_Tshaya, @IraWatt is spot on -- each entry in the dataset refers to a specific example of a patient being discharged from the hospital, and then either being readmitted or not readmitted within a certain time frame.
So yes, we need time/date information to help us define the target labels, but each row is independent of each other.
It's not a time series problem, where the rows refer to data captured for the same thing/person/entity at different points in time.
@Siya_Tshaya the problem is if they are readmitted or not, The date they are readmitted isn't what we are looking at. Each of your entrees are independent of each other making this not a time series problem I believe.
Though predicting likelihood is a percentage, your target is readmittance which is a category (was readmitted or was not readmitted) therefore it is classification.
@Siya_Tshaya if you're looking for some reading material to help you figure this out, you can check out these:
What is Regression? (from DataRobot.com)
Confusion Matrix (for multiclass models) (from the DataRobot product docs)
@Siya_Tshaya if we are predicting a “whether or not” outcome, that seems like we have 2 distinct outcomes and want to predict which outcome is more likely — a patient being readmitted, or not readmitted. Does that still seem like a regression target problem?