Changing the random_state for the data split results in a changed AUC for my RandomForestClassifier model. Is this expected? I am looking at the AUCs of three RandomForestClassifier models. Before this, I split my data using test and train using a random_state.
When I change the random_state for the data split, the AUCs changes. Is this supposed to happen or does it mean my model most likely needs parameter tuning?
Here are the AUCs using a random_state of 1 and then a random_state of 2.
Model A. 0.76 -> 0.71
Model B. 0.73 -> 0.69
Model C. 0.57 -> 0.58
Thanks.

YES
When you have one random state, you select certain data for training, develop a model, and then test the model on a holdout data set.
When you change the random state, you select different data for training, develop a different mode, and then test this other mode on a different holdout data set.
I would expect different models that are tested on different data sets to have different performance.
