I am running a Random Forest on data which has multiple observations per Subject. Some change with time, most don't.
Subject time Feature1 Feature2 Feature3 Feature4 Feature5 Feature6 Target Ann 1 Yr1 Female 20 Special-Yes 3.6 Car 0 Ann 2 Yr2 Female 21 Special-Yes 4.0 Car 0 Ann 3 Yr3 Female 22 Special-Yes 3.2 Bus 0 Bob 2 Yr1 Male 19 Special-No 2.6 Car 0 Bob 3 Yr2 Male 20 Special-No 2.7 Car 1 Cathy 2 Yr1 Female 24 Special-No 1.6 Bus 1 Diane 3 Yr1 Female 37 Special-Yes 6.6 Bus 1
Feature 3 is age, therefore, increases by 1 each year. Feature 2 and 4 never changes with time. Feature 5 changes over time. Feature 6 may or may not change.
I don't want to model the change in values over time for each subject
All I want to be able to predict if the subject belong to class 0 or 1.
What are the implications of dropping Time and Subject Name and running a Random Forest Model out the features and the target ? Is this valid ? Why not ? Can I consider each row to be independent after dropping time and Subject ?
I can see that there are image classification models that consider multiple images per Subject such as 3 different X-Rays per patient. However, target variable in these cases are consistent across the observations for each Subject, which is not in my data set.
Can I have multiple observations / records per Subject in a classification problem ? If yes, does each record have to be unique to run RF ?
I looked at this post. But couldn't find a convincing answer. My data set looks like this.