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

.. py:module:: bluecast.ml_modelling.catboost_regression


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

Classes
~~~~~~~

.. autoapisummary::

   bluecast.ml_modelling.catboost_regression.CatboostModelRegression




.. py:class:: CatboostModelRegression(class_problem: Literal[regression], conf_training: Optional[bluecast.config.training_config.TrainingConfig] = None, conf_catboost: Optional[bluecast.config.training_config.CatboostTuneParamsRegressionConfig] = None, conf_params_catboost: Optional[bluecast.config.training_config.CatboostRegressionFinalParamConfig] = 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.CatboostBaseModel`

   Train and/or tune a CatBoost regression model.

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

      Train the CatBoost regressor. Includes hyperparameter tuning by default,
      then trains on full or partial data as specified.


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


   .. py:method:: train_single_fold_model(train_pool: catboost.Pool, test_pool: catboost.Pool, y_test: pandas.Series, params: Dict[str, Any]) -> float

      Single-fold training approach. Trains a quick model and returns the
      metric from the single_fold_eval_metric_func.


   .. 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) -> float


   .. 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 using the trained CatBoost regressor.
      Returns numeric predictions.