I have a list of people, attributes about those people (height, weight, blood pressure, etc.), and a binary target variable called has_heart_issues
. This data represents the full population of data, and I am trying to determine whether anyone who is listed as "No" for has_heart_issues
is similar to the people who are listed as "Yes".
To answer this question, I split the data into training (70%) and testing (30%). I trained a random forest model on the training, and I tested it on the testing. The results are good, but I don't know how to apply to the population since I used most of it for training. Is there any way to apply the model to the full dataset (including the training) since I had labels for the full dataset to start with? Essentially, I am trying to determine whether any of the people were mislabeled.
Is it okay to apply the model to the training data to find the "mislabeled" records?