xgb_clf.py

from xgboost import XGBClassifier

xgb_clf = XGBClassifier(max_depth=12, learning_rate=1e-4,n_estimators=500)
xgb_clf.fit(X_train, np.argmax(np.array(y_train), axis = 1))
xgb_y_pred = xgb_clf.predict(X_val)

pd.Series(xgb_clf.feature_importances_, index = X_train.columns).nlargest(12).plot(kind = 'barh',
                                                                               figsize = (10, 10),
                                                                              title = 'Feature importance from XGBoost').invert_yaxis();