I have read this question: How do i interpret the output of XGBoost importance? about the three different types of feature importances: frequency (called "weight" in Python XGBoost), gain, and cover.
In my case, I have a feature, Gender, that has a very low importance based on the frequency metric, but is the most important feature by far based on both the gain, and cover metrics.
I know gender should be important for what I'm predicting. If I plot only gender vs. the target, there is a clear correlation. I am not surprised that it's the most important feature. I just want to know why it's not considered an important feature based on the frequency metric.