In a situation where interpretability is crucial, how would you approach using a Random Forest or Gradient Boosting model?
- Avoid using them entirely
- Provide feature importance scores
- Use without any explanation
- Utilize simpler base learners
In scenarios where interpretability is vital, providing feature importance scores can give insights into the contribution of each feature to the model's predictions. This approach adds an element of transparency to models like Random Forest or Gradient Boosting, which are typically considered more complex.
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