XGBoost Python 包

本页包含所有与 Python 包相关的文档链接。要安装该包，请查看安装指南。

目录

• Python 包介绍
  • 安装 XGBoost
  • 数据接口
    • 各种 XGBoost 函数支持的数据结构
      • 标记
      • 表头
      • 支持矩阵
  • 设置参数
  • 训练
  • 提前停止
  • 预测
  • 绘图
  • Scikit-Learn 接口
• 使用 Scikit-Learn 评估器接口
  • 概述
  • 提前停止
  • 获取原生 booster 对象
  • 预测
  • 并行线程数
• Python API 参考
  • 全局配置
    • config_context()
    • set_config()
    • get_config()
    • build_info()
  • 核心数据结构
    • DMatrix
      • DMatrix.data_split_mode()
      • DMatrix.feature_names
      • DMatrix.feature_types
      • DMatrix.get_base_margin()
      • DMatrix.get_data()
      • DMatrix.get_float_info()
      • DMatrix.get_group()
      • DMatrix.get_label()
      • DMatrix.get_quantile_cut()
      • DMatrix.get_uint_info()
      • DMatrix.get_weight()
      • DMatrix.num_col()
      • DMatrix.num_nonmissing()
      • 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()
    • QuantileDMatrix
      • QuantileDMatrix.data_split_mode()
      • QuantileDMatrix.feature_names
      • QuantileDMatrix.feature_types
      • QuantileDMatrix.get_base_margin()
      • QuantileDMatrix.get_data()
      • QuantileDMatrix.get_float_info()
      • QuantileDMatrix.get_group()
      • QuantileDMatrix.get_label()
      • QuantileDMatrix.get_quantile_cut()
      • QuantileDMatrix.get_uint_info()
      • QuantileDMatrix.get_weight()
      • QuantileDMatrix.num_col()
      • QuantileDMatrix.num_nonmissing()
      • QuantileDMatrix.num_row()
      • QuantileDMatrix.ref
      • QuantileDMatrix.save_binary()
      • QuantileDMatrix.set_base_margin()
      • QuantileDMatrix.set_float_info()
      • QuantileDMatrix.set_float_info_npy2d()
      • QuantileDMatrix.set_group()
      • QuantileDMatrix.set_info()
      • QuantileDMatrix.set_label()
      • QuantileDMatrix.set_uint_info()
      • QuantileDMatrix.set_weight()
      • QuantileDMatrix.slice()
    • ExtMemQuantileDMatrix
      • ExtMemQuantileDMatrix.data_split_mode()
      • ExtMemQuantileDMatrix.feature_names
      • ExtMemQuantileDMatrix.feature_types
      • ExtMemQuantileDMatrix.get_base_margin()
      • ExtMemQuantileDMatrix.get_data()
      • ExtMemQuantileDMatrix.get_float_info()
      • ExtMemQuantileDMatrix.get_group()
      • ExtMemQuantileDMatrix.get_label()
      • ExtMemQuantileDMatrix.get_quantile_cut()
      • ExtMemQuantileDMatrix.get_uint_info()
      • ExtMemQuantileDMatrix.get_weight()
      • ExtMemQuantileDMatrix.num_col()
      • ExtMemQuantileDMatrix.num_nonmissing()
      • ExtMemQuantileDMatrix.num_row()
      • ExtMemQuantileDMatrix.ref
      • ExtMemQuantileDMatrix.save_binary()
      • ExtMemQuantileDMatrix.set_base_margin()
      • ExtMemQuantileDMatrix.set_float_info()
      • ExtMemQuantileDMatrix.set_float_info_npy2d()
      • ExtMemQuantileDMatrix.set_group()
      • ExtMemQuantileDMatrix.set_info()
      • ExtMemQuantileDMatrix.set_label()
      • ExtMemQuantileDMatrix.set_uint_info()
      • ExtMemQuantileDMatrix.set_weight()
      • ExtMemQuantileDMatrix.slice()
    • Booster
      • Booster.__getitem__()
      • Booster.attr()
      • Booster.attributes()
      • Booster.best_iteration
      • Booster.best_score
      • 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.reset()
      • Booster.save_config()
      • Booster.save_model()
      • Booster.save_raw()
      • Booster.set_attr()
      • Booster.set_param()
      • Booster.trees_to_dataframe()
      • Booster.update()
    • DataIter
      • DataIter.get_callbacks()
      • DataIter.next()
      • DataIter.proxy
      • DataIter.reraise()
      • DataIter.reset()
  • 学习 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_metadata_routing()
      • 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_fit_request()
      • XGBRegressor.set_params()
      • XGBRegressor.set_predict_request()
      • XGBRegressor.set_score_request()
    • 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_metadata_routing()
      • 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_fit_request()
      • XGBClassifier.set_params()
      • XGBClassifier.set_predict_proba_request()
      • XGBClassifier.set_predict_request()
      • XGBClassifier.set_score_request()
    • 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_metadata_routing()
      • 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.score()
      • XGBRanker.set_fit_request()
      • XGBRanker.set_params()
      • XGBRanker.set_predict_request()
    • 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_metadata_routing()
      • 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_fit_request()
      • XGBRFRegressor.set_params()
      • XGBRFRegressor.set_predict_request()
      • XGBRFRegressor.set_score_request()
    • 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_metadata_routing()
      • 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_fit_request()
      • XGBRFClassifier.set_params()
      • XGBRFClassifier.set_predict_proba_request()
      • XGBRFClassifier.set_predict_request()
      • XGBRFClassifier.set_score_request()
  • 绘图 API
    • plot_importance()
    • plot_tree()
    • to_graphviz()
  • 回调 API
    • TrainingCallback
      • TrainingCallback.after_iteration()
      • TrainingCallback.after_training()
      • TrainingCallback.before_iteration()
      • TrainingCallback.before_training()
    • EvaluationMonitor
      • EvaluationMonitor.after_iteration()
      • EvaluationMonitor.after_training()
    • EarlyStopping
      • EarlyStopping.after_iteration()
      • EarlyStopping.after_training()
      • EarlyStopping.before_training()
    • LearningRateScheduler
      • LearningRateScheduler.after_iteration()
    • TrainingCheckPoint
      • TrainingCheckPoint.after_iteration()
      • TrainingCheckPoint.before_training()
  • Dask API
  • PySpark API
    • SparkXGBClassifier
      • SparkXGBClassifier.clear()
      • SparkXGBClassifier.copy()
      • SparkXGBClassifier.explainParam()
      • SparkXGBClassifier.explainParams()
      • SparkXGBClassifier.extractParamMap()
      • SparkXGBClassifier.fit()
      • SparkXGBClassifier.fitMultiple()
      • SparkXGBClassifier.getFeaturesCol()
      • SparkXGBClassifier.getLabelCol()
      • SparkXGBClassifier.getOrDefault()
      • SparkXGBClassifier.getParam()
      • SparkXGBClassifier.getPredictionCol()
      • SparkXGBClassifier.getProbabilityCol()
      • SparkXGBClassifier.getRawPredictionCol()
      • SparkXGBClassifier.getValidationIndicatorCol()
      • SparkXGBClassifier.getWeightCol()
      • SparkXGBClassifier.hasDefault()
      • SparkXGBClassifier.hasParam()
      • SparkXGBClassifier.isDefined()
      • SparkXGBClassifier.isSet()
      • SparkXGBClassifier.load()
      • SparkXGBClassifier.params
      • SparkXGBClassifier.read()
      • SparkXGBClassifier.save()
      • SparkXGBClassifier.set()
      • SparkXGBClassifier.setParams()
      • SparkXGBClassifier.set_coll_cfg()
      • SparkXGBClassifier.set_device()
      • SparkXGBClassifier.uid
      • SparkXGBClassifier.write()
    • SparkXGBClassifierModel
      • SparkXGBClassifierModel.clear()
      • SparkXGBClassifierModel.copy()
      • SparkXGBClassifierModel.explainParam()
      • SparkXGBClassifierModel.explainParams()
      • SparkXGBClassifierModel.extractParamMap()
      • SparkXGBClassifierModel.getFeaturesCol()
      • SparkXGBClassifierModel.getLabelCol()
      • SparkXGBClassifierModel.getOrDefault()
      • SparkXGBClassifierModel.getParam()
      • SparkXGBClassifierModel.getPredictionCol()
      • SparkXGBClassifierModel.getProbabilityCol()
      • SparkXGBClassifierModel.getRawPredictionCol()
      • SparkXGBClassifierModel.getValidationIndicatorCol()
      • SparkXGBClassifierModel.getWeightCol()
      • SparkXGBClassifierModel.get_booster()
      • SparkXGBClassifierModel.get_feature_importances()
      • SparkXGBClassifierModel.hasDefault()
      • SparkXGBClassifierModel.hasParam()
      • SparkXGBClassifierModel.isDefined()
      • SparkXGBClassifierModel.isSet()
      • SparkXGBClassifierModel.load()
      • SparkXGBClassifierModel.params
      • SparkXGBClassifierModel.read()
      • SparkXGBClassifierModel.save()
      • SparkXGBClassifierModel.set()
      • SparkXGBClassifierModel.set_coll_cfg()
      • SparkXGBClassifierModel.set_device()
      • SparkXGBClassifierModel.transform()
      • SparkXGBClassifierModel.uid
      • SparkXGBClassifierModel.write()
    • SparkXGBRegressor
      • SparkXGBRegressor.clear()
      • SparkXGBRegressor.copy()
      • SparkXGBRegressor.explainParam()
      • SparkXGBRegressor.explainParams()
      • SparkXGBRegressor.extractParamMap()
      • SparkXGBRegressor.fit()
      • SparkXGBRegressor.fitMultiple()
      • SparkXGBRegressor.getFeaturesCol()
      • SparkXGBRegressor.getLabelCol()
      • SparkXGBRegressor.getOrDefault()
      • SparkXGBRegressor.getParam()
      • SparkXGBRegressor.getPredictionCol()
      • SparkXGBRegressor.getValidationIndicatorCol()
      • SparkXGBRegressor.getWeightCol()
      • SparkXGBRegressor.hasDefault()
      • SparkXGBRegressor.hasParam()
      • SparkXGBRegressor.isDefined()
      • SparkXGBRegressor.isSet()
      • SparkXGBRegressor.load()
      • SparkXGBRegressor.params
      • SparkXGBRegressor.read()
      • SparkXGBRegressor.save()
      • SparkXGBRegressor.set()
      • SparkXGBRegressor.setParams()
      • SparkXGBRegressor.set_coll_cfg()
      • SparkXGBRegressor.set_device()
      • SparkXGBRegressor.uid
      • SparkXGBRegressor.write()
    • SparkXGBRegressorModel
      • SparkXGBRegressorModel.clear()
      • SparkXGBRegressorModel.copy()
      • SparkXGBRegressorModel.explainParam()
      • SparkXGBRegressorModel.explainParams()
      • SparkXGBRegressorModel.extractParamMap()
      • SparkXGBRegressorModel.getFeaturesCol()
      • SparkXGBRegressorModel.getLabelCol()
      • SparkXGBRegressorModel.getOrDefault()
      • SparkXGBRegressorModel.getParam()
      • SparkXGBRegressorModel.getPredictionCol()
      • SparkXGBRegressorModel.getValidationIndicatorCol()
      • SparkXGBRegressorModel.getWeightCol()
      • SparkXGBRegressorModel.get_booster()
      • SparkXGBRegressorModel.get_feature_importances()
      • SparkXGBRegressorModel.hasDefault()
      • SparkXGBRegressorModel.hasParam()
      • SparkXGBRegressorModel.isDefined()
      • SparkXGBRegressorModel.isSet()
      • SparkXGBRegressorModel.load()
      • SparkXGBRegressorModel.params
      • SparkXGBRegressorModel.read()
      • SparkXGBRegressorModel.save()
      • SparkXGBRegressorModel.set()
      • SparkXGBRegressorModel.set_coll_cfg()
      • SparkXGBRegressorModel.set_device()
      • SparkXGBRegressorModel.transform()
      • SparkXGBRegressorModel.uid
      • SparkXGBRegressorModel.write()
    • SparkXGBRanker
      • SparkXGBRanker.clear()
      • SparkXGBRanker.copy()
      • SparkXGBRanker.explainParam()
      • SparkXGBRanker.explainParams()
      • SparkXGBRanker.extractParamMap()
      • SparkXGBRanker.fit()
      • SparkXGBRanker.fitMultiple()
      • SparkXGBRanker.getFeaturesCol()
      • SparkXGBRanker.getLabelCol()
      • SparkXGBRanker.getOrDefault()
      • SparkXGBRanker.getParam()
      • SparkXGBRanker.getPredictionCol()
      • SparkXGBRanker.getValidationIndicatorCol()
      • SparkXGBRanker.getWeightCol()
      • SparkXGBRanker.hasDefault()
      • SparkXGBRanker.hasParam()
      • SparkXGBRanker.isDefined()
      • SparkXGBRanker.isSet()
      • SparkXGBRanker.load()
      • SparkXGBRanker.params
      • SparkXGBRanker.read()
      • SparkXGBRanker.save()
      • SparkXGBRanker.set()
      • SparkXGBRanker.setParams()
      • SparkXGBRanker.set_coll_cfg()
      • SparkXGBRanker.set_device()
      • SparkXGBRanker.uid
      • SparkXGBRanker.write()
    • SparkXGBRankerModel
      • SparkXGBRankerModel.clear()
      • SparkXGBRankerModel.copy()
      • SparkXGBRankerModel.explainParam()
      • SparkXGBRankerModel.explainParams()
      • SparkXGBRankerModel.extractParamMap()
      • SparkXGBRankerModel.getFeaturesCol()
      • SparkXGBRankerModel.getLabelCol()
      • SparkXGBRankerModel.getOrDefault()
      • SparkXGBRankerModel.getParam()
      • SparkXGBRankerModel.getPredictionCol()
      • SparkXGBRankerModel.getValidationIndicatorCol()
      • SparkXGBRankerModel.getWeightCol()
      • SparkXGBRankerModel.get_booster()
      • SparkXGBRankerModel.get_feature_importances()
      • SparkXGBRankerModel.hasDefault()
      • SparkXGBRankerModel.hasParam()
      • SparkXGBRankerModel.isDefined()
      • SparkXGBRankerModel.isSet()
      • SparkXGBRankerModel.load()
      • SparkXGBRankerModel.params
      • SparkXGBRankerModel.read()
      • SparkXGBRankerModel.save()
      • SparkXGBRankerModel.set()
      • SparkXGBRankerModel.set_coll_cfg()
      • SparkXGBRankerModel.set_device()
      • SparkXGBRankerModel.transform()
      • SparkXGBRankerModel.uid
      • SparkXGBRankerModel.write()
  • Collective
    • Config
      • Config.retry
      • Config.timeout
      • Config.tracker_host_ip
      • Config.tracker_port
      • Config.tracker_timeout
    • init()
    • RabitTracker
• 回调函数
  • 使用内置回调
  • 定义自己的回调
• 模型
  • 切片树模型
• XGBoost Python 特性演练
• XGBoost Dask 特性演练
• 生存分析演练
• GPU 加速演示
• 将 XGBoost 与 RAPIDS Memory Manager (RMM) 插件一起使用
  • 告知 XGBoost 关于 RMM 池的信息
  • 多 GPU 无设备序号
  • 内存超额订阅