:py:mod:`bluecast.blueprints.custom_model_recipes` ================================================== .. py:module:: bluecast.blueprints.custom_model_recipes Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: bluecast.blueprints.custom_model_recipes.LogisticRegressionModel bluecast.blueprints.custom_model_recipes.LinearRegressionModel .. py:class:: LogisticRegressionModel(max_iter=100000, random_state=300) Bases: :py:obj:`bluecast.ml_modelling.base_classes.BaseClassMlModel` Base class for all ML models. Enforces the implementation of the fit and predict methods. If hyperparameter tuning is required, then the fit method should implement the tuning. .. py:method:: autotune(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) .. py:method:: fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> None .. py:method:: predict(df: pandas.DataFrame) -> Tuple[bluecast.ml_modelling.base_classes.PredictedProbas, bluecast.ml_modelling.base_classes.PredictedClasses] Predict on unseen data. :return tuple of predicted probabilities and predicted classes .. py:class:: LinearRegressionModel Bases: :py:obj:`bluecast.ml_modelling.base_classes.BaseClassMlModel` Base class for all ML models. Enforces the implementation of the fit and predict methods. If hyperparameter tuning is required, then the fit method should implement the tuning. .. py:method:: autotune(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) .. py:method:: fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> None .. py:method:: predict(df: pandas.DataFrame) -> Tuple[bluecast.ml_modelling.base_classes.PredictedProbas, bluecast.ml_modelling.base_classes.PredictedClasses] Predict on unseen data. :return tuple of predicted probabilities and predicted classes