:py:mod:`bluecast.ml_modelling.xgboost_regression`
==================================================

.. py:module:: bluecast.ml_modelling.xgboost_regression

.. autoapi-nested-parse::

   Xgboost classification model.

   This module contains a wrapper for the Xgboost classification model. It can be used to train and/or tune the model.
   It also calculates class weights for imbalanced datasets. The weights may or may not be used deepending on the
   hyperparameter tuning.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   bluecast.ml_modelling.xgboost_regression.XgboostModelRegression




.. py:class:: XgboostModelRegression(class_problem: Literal[regression], conf_training: Optional[bluecast.config.training_config.TrainingConfig] = None, conf_xgboost: Optional[bluecast.config.training_config.XgboostTuneParamsRegressionConfig] = None, conf_params_xgboost: Optional[bluecast.config.training_config.XgboostRegressionFinalParamConfig] = None, experiment_tracker: Optional[bluecast.experimentation.tracking.ExperimentTracker] = None, custom_in_fold_preprocessor: Optional[bluecast.preprocessing.custom.CustomPreprocessing] = None, cat_columns: Optional[List[Union[str, float, int]]] = None, single_fold_eval_metric_func: Optional[bluecast.evaluation.eval_metrics.RegressionEvalWrapper] = None)


   Bases: :py:obj:`bluecast.ml_modelling.base_classes.XgboostBaseModel`

   Train and/or tune Xgboost regression model.

   .. py:method:: fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> xgboost.Booster

      Train Xgboost model. Includes hyperparameter tuning on default.


   .. py:method:: autotune(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> None

      Tune hyperparameters.

      An alternative config can be provided to overwrite the hyperparameter search space.


   .. py:method:: train_single_fold_model(d_train, d_test, y_test, param, steps, pruning_callback)


   .. py:method:: _fine_tune_precise(tuned_params: Dict[str, Any], x_train: pandas.DataFrame, y_train: pandas.Series, x_test: pandas.DataFrame, y_test: pandas.Series)


   .. py:method:: fine_tune(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> None


   .. py:method:: predict(df: pandas.DataFrame) -> numpy.ndarray

      Predict on unseen data.