# Analysis of Feature Importances when features are dependent on one another

I can use random forests to determine which features are important when doing a prediction problem; for example.

< height, weight, IQ measure> -> Is considered obese?

Applying random forests could tell me that weight contributes 0.75 percent to obesity.

But what if weight was dependent on height? For example weight = height^2 + IQ Measure^3/height or something?

How would this affect the generated feature importances? Would this 'hidden' relationship have any consequences?

I just thought about this, and was wondering whether this could be an issue. As in, my feature importances would be inaccurate.