XGBoost Python Package

This page contains links to all the python related documents on python package. To install the package, checkout Installation Guide.

Contents

• Python Package Introduction
  • Install XGBoost
  • Data Interface
  • Setting Parameters
  • Training
  • Early Stopping
  • Prediction
  • Plotting
  • Scikit-Learn interface
• Python API Reference
  • Global Configuration
    • config_context()
    • set_config()
    • get_config()
  • Core Data Structure
    • DMatrix
      • DMatrix.feature_names
      • DMatrix.feature_types
      • DMatrix.get_base_margin()
      • DMatrix.get_float_info()
      • DMatrix.get_group()
      • DMatrix.get_label()
      • DMatrix.get_uint_info()
      • DMatrix.get_weight()
      • DMatrix.num_col()
      • DMatrix.num_row()
      • DMatrix.save_binary()
      • DMatrix.set_base_margin()
      • DMatrix.set_float_info()
      • DMatrix.set_float_info_npy2d()
      • DMatrix.set_group()
      • DMatrix.set_info()
      • DMatrix.set_label()
      • DMatrix.set_uint_info()
      • DMatrix.set_weight()
      • DMatrix.slice()
    • DeviceQuantileDMatrix
    • Booster
      • Booster.attr()
      • Booster.attributes()
      • Booster.boost()
      • Booster.copy()
      • Booster.dump_model()
      • Booster.eval()
      • Booster.eval_set()
      • Booster.feature_names
      • Booster.feature_types
      • Booster.get_dump()
      • Booster.get_fscore()
      • Booster.get_score()
      • Booster.get_split_value_histogram()
      • Booster.inplace_predict()
      • Booster.load_config()
      • Booster.load_model()
      • Booster.num_boosted_rounds()
      • Booster.num_features()
      • Booster.predict()
      • Booster.save_config()
      • Booster.save_model()
      • Booster.save_raw()
      • Booster.set_attr()
      • Booster.set_param()
      • Booster.trees_to_dataframe()
      • Booster.update()
  • Learning API
    • train()
    • cv()
  • Scikit-Learn API
    • XGBRegressor
      • XGBRegressor.apply()
      • XGBRegressor.best_iteration
      • XGBRegressor.best_score
      • XGBRegressor.coef_
      • XGBRegressor.evals_result()
      • XGBRegressor.feature_importances_
      • XGBRegressor.feature_names_in_
      • XGBRegressor.fit()
      • XGBRegressor.get_booster()
      • XGBRegressor.get_num_boosting_rounds()
      • XGBRegressor.get_params()
      • XGBRegressor.get_xgb_params()
      • XGBRegressor.intercept_
      • XGBRegressor.load_model()
      • XGBRegressor.n_features_in_
      • XGBRegressor.predict()
      • XGBRegressor.save_model()
      • XGBRegressor.score()
      • XGBRegressor.set_params()
    • XGBClassifier
      • XGBClassifier.apply()
      • XGBClassifier.best_iteration
      • XGBClassifier.best_score
      • XGBClassifier.coef_
      • XGBClassifier.evals_result()
      • XGBClassifier.feature_importances_
      • XGBClassifier.feature_names_in_
      • XGBClassifier.fit()
      • XGBClassifier.get_booster()
      • XGBClassifier.get_num_boosting_rounds()
      • XGBClassifier.get_params()
      • XGBClassifier.get_xgb_params()
      • XGBClassifier.intercept_
      • XGBClassifier.load_model()
      • XGBClassifier.n_features_in_
      • XGBClassifier.predict()
      • XGBClassifier.predict_proba()
      • XGBClassifier.save_model()
      • XGBClassifier.score()
      • XGBClassifier.set_params()
    • XGBRanker
      • XGBRanker.apply()
      • XGBRanker.best_iteration
      • XGBRanker.best_score
      • XGBRanker.coef_
      • XGBRanker.evals_result()
      • XGBRanker.feature_importances_
      • XGBRanker.feature_names_in_
      • XGBRanker.fit()
      • XGBRanker.get_booster()
      • XGBRanker.get_num_boosting_rounds()
      • XGBRanker.get_params()
      • XGBRanker.get_xgb_params()
      • XGBRanker.intercept_
      • XGBRanker.load_model()
      • XGBRanker.n_features_in_
      • XGBRanker.predict()
      • XGBRanker.save_model()
      • XGBRanker.set_params()
    • XGBRFRegressor
      • XGBRFRegressor.apply()
      • XGBRFRegressor.best_iteration
      • XGBRFRegressor.best_score
      • XGBRFRegressor.coef_
      • XGBRFRegressor.evals_result()
      • XGBRFRegressor.feature_importances_
      • XGBRFRegressor.feature_names_in_
      • XGBRFRegressor.fit()
      • XGBRFRegressor.get_booster()
      • XGBRFRegressor.get_num_boosting_rounds()
      • XGBRFRegressor.get_params()
      • XGBRFRegressor.get_xgb_params()
      • XGBRFRegressor.intercept_
      • XGBRFRegressor.load_model()
      • XGBRFRegressor.n_features_in_
      • XGBRFRegressor.predict()
      • XGBRFRegressor.save_model()
      • XGBRFRegressor.score()
      • XGBRFRegressor.set_params()
    • XGBRFClassifier
      • XGBRFClassifier.apply()
      • XGBRFClassifier.best_iteration
      • XGBRFClassifier.best_score
      • XGBRFClassifier.coef_
      • XGBRFClassifier.evals_result()
      • XGBRFClassifier.feature_importances_
      • XGBRFClassifier.feature_names_in_
      • XGBRFClassifier.fit()
      • XGBRFClassifier.get_booster()
      • XGBRFClassifier.get_num_boosting_rounds()
      • XGBRFClassifier.get_params()
      • XGBRFClassifier.get_xgb_params()
      • XGBRFClassifier.intercept_
      • XGBRFClassifier.load_model()
      • XGBRFClassifier.n_features_in_
      • XGBRFClassifier.predict()
      • XGBRFClassifier.predict_proba()
      • XGBRFClassifier.save_model()
      • XGBRFClassifier.score()
      • XGBRFClassifier.set_params()
  • Plotting API
    • plot_importance()
    • plot_tree()
    • to_graphviz()
  • Callback API
    • TrainingCallback
      • TrainingCallback.after_iteration()
      • TrainingCallback.after_training()
      • TrainingCallback.before_iteration()
      • TrainingCallback.before_training()
    • EvaluationMonitor
      • EvaluationMonitor.after_iteration()
      • EvaluationMonitor.after_training()
      • EvaluationMonitor.before_iteration()
      • EvaluationMonitor.before_training()
    • EarlyStopping
      • EarlyStopping.after_iteration()
      • EarlyStopping.after_training()
      • EarlyStopping.before_iteration()
      • EarlyStopping.before_training()
    • LearningRateScheduler
      • LearningRateScheduler.after_iteration()
      • LearningRateScheduler.after_training()
      • LearningRateScheduler.before_iteration()
      • LearningRateScheduler.before_training()
    • TrainingCheckPoint
      • TrainingCheckPoint.after_iteration()
      • TrainingCheckPoint.after_training()
      • TrainingCheckPoint.before_iteration()
      • TrainingCheckPoint.before_training()
  • Dask API
    • Dask extensions for distributed training
      • Optional dask configuration
    • DaskDMatrix
    • DaskDeviceQuantileDMatrix
    • train()
    • predict()
    • inplace_predict()
    • DaskXGBClassifier
      • DaskXGBClassifier.apply()
      • DaskXGBClassifier.best_iteration
      • DaskXGBClassifier.best_score
      • DaskXGBClassifier.client
      • DaskXGBClassifier.coef_
      • DaskXGBClassifier.evals_result()
      • DaskXGBClassifier.feature_importances_
      • DaskXGBClassifier.feature_names_in_
      • DaskXGBClassifier.fit()
      • DaskXGBClassifier.get_booster()
      • DaskXGBClassifier.get_num_boosting_rounds()
      • DaskXGBClassifier.get_params()
      • DaskXGBClassifier.get_xgb_params()
      • DaskXGBClassifier.intercept_
      • DaskXGBClassifier.load_model()
      • DaskXGBClassifier.n_features_in_
      • DaskXGBClassifier.predict()
      • DaskXGBClassifier.predict_proba()
      • DaskXGBClassifier.save_model()
      • DaskXGBClassifier.score()
      • DaskXGBClassifier.set_params()
    • DaskXGBRegressor
      • DaskXGBRegressor.apply()
      • DaskXGBRegressor.best_iteration
      • DaskXGBRegressor.best_score
      • DaskXGBRegressor.client
      • DaskXGBRegressor.coef_
      • DaskXGBRegressor.evals_result()
      • DaskXGBRegressor.feature_importances_
      • DaskXGBRegressor.feature_names_in_
      • DaskXGBRegressor.fit()
      • DaskXGBRegressor.get_booster()
      • DaskXGBRegressor.get_num_boosting_rounds()
      • DaskXGBRegressor.get_params()
      • DaskXGBRegressor.get_xgb_params()
      • DaskXGBRegressor.intercept_
      • DaskXGBRegressor.load_model()
      • DaskXGBRegressor.n_features_in_
      • DaskXGBRegressor.predict()
      • DaskXGBRegressor.save_model()
      • DaskXGBRegressor.score()
      • DaskXGBRegressor.set_params()
    • DaskXGBRanker
      • DaskXGBRanker.apply()
      • DaskXGBRanker.best_iteration
      • DaskXGBRanker.best_score
      • DaskXGBRanker.client
      • DaskXGBRanker.coef_
      • DaskXGBRanker.evals_result()
      • DaskXGBRanker.feature_importances_
      • DaskXGBRanker.feature_names_in_
      • DaskXGBRanker.fit()
      • DaskXGBRanker.get_booster()
      • DaskXGBRanker.get_num_boosting_rounds()
      • DaskXGBRanker.get_params()
      • DaskXGBRanker.get_xgb_params()
      • DaskXGBRanker.intercept_
      • DaskXGBRanker.load_model()
      • DaskXGBRanker.n_features_in_
      • DaskXGBRanker.predict()
      • DaskXGBRanker.save_model()
      • DaskXGBRanker.set_params()
    • DaskXGBRFRegressor
      • DaskXGBRFRegressor.apply()
      • DaskXGBRFRegressor.best_iteration
      • DaskXGBRFRegressor.best_score
      • DaskXGBRFRegressor.client
      • DaskXGBRFRegressor.coef_
      • DaskXGBRFRegressor.evals_result()
      • DaskXGBRFRegressor.feature_importances_
      • DaskXGBRFRegressor.feature_names_in_
      • DaskXGBRFRegressor.fit()
      • DaskXGBRFRegressor.get_booster()
      • DaskXGBRFRegressor.get_num_boosting_rounds()
      • DaskXGBRFRegressor.get_params()
      • DaskXGBRFRegressor.get_xgb_params()
      • DaskXGBRFRegressor.intercept_
      • DaskXGBRFRegressor.load_model()
      • DaskXGBRFRegressor.n_features_in_
      • DaskXGBRFRegressor.predict()
      • DaskXGBRFRegressor.save_model()
      • DaskXGBRFRegressor.score()
      • DaskXGBRFRegressor.set_params()
    • DaskXGBRFClassifier
      • DaskXGBRFClassifier.apply()
      • DaskXGBRFClassifier.best_iteration
      • DaskXGBRFClassifier.best_score
      • DaskXGBRFClassifier.client
      • DaskXGBRFClassifier.coef_
      • DaskXGBRFClassifier.evals_result()
      • DaskXGBRFClassifier.feature_importances_
      • DaskXGBRFClassifier.feature_names_in_
      • DaskXGBRFClassifier.fit()
      • DaskXGBRFClassifier.get_booster()
      • DaskXGBRFClassifier.get_num_boosting_rounds()
      • DaskXGBRFClassifier.get_params()
      • DaskXGBRFClassifier.get_xgb_params()
      • DaskXGBRFClassifier.intercept_
      • DaskXGBRFClassifier.load_model()
      • DaskXGBRFClassifier.n_features_in_
      • DaskXGBRFClassifier.predict()
      • DaskXGBRFClassifier.predict_proba()
      • DaskXGBRFClassifier.save_model()
      • DaskXGBRFClassifier.score()
      • DaskXGBRFClassifier.set_params()
• Callback Functions
  • Using builtin callbacks
  • Defining your own callback
• Model
  • Slice tree model
• XGBoost Python Feature Walkthrough
• XGBoost Dask Feature Walkthrough