How to apply model to training data to identify mislabeled observations? 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?
Sure, its called cross validation. Have a look