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pythonpandasmachine-learningxgboost

XGBoost plot_importance doesn't show feature names

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, \
                                    test_size=0.2, random_state=42)
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.

enter image description here

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?

Solution

  • You want to use the feature_names parameter when creating your xgb.DMatrix

    dtrain = xgb.DMatrix(Xtrain, label=ytrain, feature_names=feature_names)