AdaBoostClassifier (ABC)
========================

.. class:: ABC(self, model_name: str = 'AdaBoostClassifier', random_state: int = 42, estimator: Union[Literal['DTC', 'RFC', 'LR'], sklearn.base.ClassifierMixin] = 'DTC', **kwargs)

AdaBoostClassifier Wrapper class  - parent class :class:`Classifier`

.. list-table::
   :widths: 25 75
   :header-rows: 0

   * - Parameters
     - 
        estimator : {"DTC", "RFC", "LR"} or classifier object,                 default="DTC"
            base estimator from which the boosted ensemble is built (default: DecisionTreeClassifier with max_depth=1)
        n_estimator : int
            number of boosting stages to perform
        learning_rate : float
            shrinks the contribution of each tree by learning rate
        algorithm : str
            boosting algorithm
        random_state : int,                 default=42
            random_state for model
        


   * - Attributes
     - 
         
        cv_scores : dict[str, float]
            dictionary with cross validation results
        
       
         
        feature_names : list[str]
            names of all the features that the model saw during training. Is empty if model was not fitted yet.
        
       
         
        grid : ConfigurationSpace
            hyperparameter tuning grid of the model
        
       
         
        model : model object
            model with 'fit', 'predict', 'set_params', and 'get_params' method (see sklearn API)
        
       
         
        model_name : str
            name of the model. Used in loading bars and dictionaries as identifier of the model
        
       
         
        model_type : str
            kind of estimator (e.g. 'RFC' for RandomForestClassifier)
        
       
         
        rCVsearch_results : pd.DataFrame or None
            results from randomCV hyperparameter tuning. Is ``None`` if randomCVsearch was not used yet.
        
       
         
        train_score : float
            train score value
        
       
         
        train_time : str
            train time in format: "0:00:00" (hours:minutes:seconds)
        
       

.. note::

   You can use all parameters of the wrapped model when initialising the wrapper class.

.. raw:: html

   <h2>Example</h2>

>>> from sam_ml.models.classifier import ABC
>>>
>>> model = ABC()
>>> print(model)
ABC(estimator=DecisionTreeClassifier(max_depth=1), model_name='AdaBoostClassifier (DTC based)')
        

.. raw:: html

   <h2>Methods</h2>

.. list-table::
   :widths: 25 75
   :header-rows: 1

   * - Method
     - Description
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.cross_validation`
     - Random split crossvalidation
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.cross_validation_small_data`
     - One-vs-all cross validation for small datasets
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.evaluate`
     - Function to create multiple scores with predict function of model
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.evaluate_proba`
     - Function to create multiple scores for binary classification with predict_proba function of model
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.evaluate_score`
     - Function to create a score with predict function of model
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.evaluate_score_proba`
     - Function to create a score for binary classification with predict_proba function of model
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.feature_importance`
     - Function to generate a matplotlib plot of the top45 feature importance from the model.
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.fit`
     - Function to fit the model
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.fit_warm_start`
     - Function to warm_start fit the model
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.get_deepcopy`
     - Function to create a deepcopy of object
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.get_params`
     - Function to get the parameter from the model object
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.get_random_config`
     - Function to generate one grid configuration
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.get_random_configs`
     - Function to generate grid configurations
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.load_model`
     - Function to load a pickled model class object
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.predict`
     - Function to predict with predict-method from model object
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.predict_proba`
     - Function to predict with predict_proba-method from model object
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.randomCVsearch`
     - Hyperparametertuning with randomCVsearch
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.replace_grid`
     - Function to replace self.grid
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.save_model`
     - Function to pickle and save the class object
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.set_params`
     - Function to set the parameter of the model object
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.smac_search`
     - Hyperparametertuning with SMAC library HyperparameterOptimizationFacade [can only be used in the sam_ml version with swig]
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.train`
     - Function to train the model
   
   * - :meth:`~sam_ml.models.classifier.AdaBoostClassifier.ABC.train_warm_start`
     - Function to warm_start train the model
   

.. note::

   A lot of methods use parameters for advanced scoring. For additional information on advanced scoring, see :ref:`scoring documentation <classifier-scoring-section>`


.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.cross_validation

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.cross_validation_small_data

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.evaluate

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.evaluate_proba

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.evaluate_score

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.evaluate_score_proba

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.feature_importance

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.fit

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.fit_warm_start

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.get_deepcopy

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.get_params

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.get_random_config

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.get_random_configs

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.load_model

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.predict

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.predict_proba

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.randomCVsearch

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.replace_grid

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.save_model

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.set_params

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.smac_search

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.train

.. automethod:: sam_ml.models.classifier.AdaBoostClassifier.ABC.train_warm_start