As an example
Let's assume we do have feature-columns $X_1$ and $X_2$ and $X_3$ and target $Y$, and it just so happens that $Y$ is the noisy spearman correlation of the three features. Would it help if you manually added the spearman correlation as a fourth feature? Or will gradient boosting 'learn' this anyway and adding it manually is not needed?
- Are there general rules of thumb which kind of feature engineering helps gradient boosting and which doesn't?
- When should one stop adding features? Is there a maximum number of features (dimensionality) where gradient boosting starts to lose effectiveness?