What does 'flat feature importance' (e.g., 10-12% each across 26 features) indicate in an XGBoost model?

Question

MLQuiz · Accepted Answer

Flat importance (~10% each for 26 features) means features overlap in information content. No dominant signal exists. This is common when all features derive from the same source (e.g., OHLCV price data).