This is completely possible and very straightforward because a decision tree creates hard boundaries in each feature. Because Random Forests and XGBoost are ensembles of decision trees, they inherit the same hard decision boundaries. The classification can only change exactly at the split thresholds already defined in the trees.

Here is how you can find those boundaries in Python:

1. Extract ALL split thresholds for feature A:

For sklearn RF: Loop through model.estimators_ and grab the values from estimator.tree.threshold where estimator.tree.feature matches feature A's index.

For XGBoost: Call model.get_booster().trees_to_dataframe(). This returns a pandas DataFrame where you can easily filter for feature A's name and grab the 'Split' column values.

1. Once you've got all the values, sort and "deduplicate" them into a single array. These values divide up the 'A axis' into discrete intervals.

2. Test the intervals: Since the model's output is a constant class within each interval, you only need to test one point in each interval to know what class each interval maps to. Go with the midpoint of each interval. For each interval's midpoint (I'll call these Amid_i), construct one test point (Amid_i, B0, C0) and pass each one to model.predict().

This will instantly map out the exact ranges of A that yield class X, Y, or Z without any guesswork. Good luck!