What can go wrong with random forest feature importances?
PICTURE THIS: DOM IS A TREE
Simple meaning
Impurity importance favors high-cardinality and correlated features.
WHY — Random Forest instead of guessing?
Why interviewers care about Random Forest:
people who only read docs from people who shipped.
and tied to AI / ML work.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1Impurity importance favors high-cardinality
and correlated features.
- 2Permutation importance is fairer
but still unstable with collinear columns.
- 3Prefer grouped permutation or
SHAP-style explanations when features are redundant.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Impurity importance favors high-cardinality and correlated features. Permutation importance is fairer but still unstable with collinear columns.