I'm using XGBoost with Python and have successfully trained a model using the XGBoost train() function called on DMatrix data. The matrix was created from a Pandas dataframe, which has feature names for the columns.
Xtrain, Xval, ytrain, yval = train_test_split(df[feature_names], y, \
dtrain = xgb.DMatrix(Xtrain, label=ytrain)
model = xgb.train(xgb_params, dtrain, num_boost_round=60, \
early_stopping_rounds=50, maximize=False, verbose_eval=10)
fig, ax = plt.subplots(1,1,figsize=(10,10))
xgb.plot_importance(model, max_num_features=5, ax=ax)
I want to now see the feature importance using the xgboost.plot_importance() function, but the resulting plot doesn't show the feature names. Instead, the features are listed as f1, f2, f3, etc. as shown below.
I think the problem is that I converted my original Pandas data frame into a DMatrix. How can I associate feature names properly so that the feature importance plot shows them?