How to get feature importance in xgboost?

In your code you can get feature importance for each feature in dict form:

bst.get_score(importance_type="gain")

>>{'ftr_col1': 77.21064539577829,
   'ftr_col2': 10.28690566363971,
   'ftr_col3': 24.225014841466294,
   'ftr_col4': 11.234086283060112}

Explanation: The train() API’s method get_score() is defined as:

get_score(fmap=”, importance_type=”weight”)

• fmap (str (optional)) – The name of feature map file.
• importance_type
  • ‘weight’ – the number of times a feature is used to split the data across all trees.
  • ‘gain’ – the average gain across all splits the feature is used in.
  • ‘cover’ – the average coverage across all splits the feature is used in.
  • ‘total_gain’ – the total gain across all splits the feature is used in.
  • ‘total_cover’ – the total coverage across all splits the feature is used in.

https://xgboost.readthedocs.io/en/latest/python/python_api.html