Why Does XGBoost Keep One Feature at High Importance?
According to the docs &quot;gain&quot; is the default, which is calculated by this formula, where I don't think intuition would help. General consensus is, that feature importance is a tricky concept, as long as your model performs fine, you shouldn't worry about it. Especially with the linked formula for gain you can see that the first split may be the most important by far, depending on gamma, so that every other feature importance is low by default.
If, on the other hand you chose &quot;weight&quot;, that would be just be the number of times the feature is chosen to split. That may mean that one feature contains a lot of unique values and can be chosen a lot of times and still improve accuracy.
All of this depends heavily on the data and parameters you are using. There isn't any general answer to this pattern.