Selector
========

.. class:: Selector(self, algorithm: Literal['kbest', 'kbest_chi2', 'pca', 'wrapper', 'sequential', 'select_model', 'rfe', 'rfecv'] = 'kbest', num_features: int = 10, estimator=LinearSVC(dual=False, penalty='l1'), **kwargs)

feature selection algorithm Wrapper class  - parent class :class:`Data`

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

   * - Parameters
     - 
        algorithm : {"kbest", "kbest_chi2", "pca", "wrapper", "sequential", "select_model", "rfe", "rfecv"},                 default="kbest"
            which selecting algorithm to use:
            - 'kbest': SelectKBest
            - 'kbest_chi2': SelectKBest with score_func=chi2 (only non-negative values)
            - 'pca': PCA (new column names after transformation)
            - 'wrapper': uses p-values of Ordinary Linear Model from statsmodels library (no num_features parameter -> problems with too many features)
            - 'sequential': SequentialFeatureSelector
            - 'select_model': SelectFromModel (meta-transformer for selecting features based on importance weights)
            - 'rfe': RFE (recursive feature elimination)
            - 'rfecv': RFECV (recursive feature elimination with cross-validation)

        num_features : int,                 default=10
            number of features to select
        estimator : estimator instance
            parameter is needed for SequentialFeatureSelector, SelectFromModel, RFE, RFECV (default: LinearSVC)
        \*\*kwargs:
            additional parameters for selector
        

   * - Attributes
     - 
         
        algorithm : str
            name of the used algorithm
        
       
         
        num_features : int
            number of features to select
        
       
         
        selected_features : list[str]
            list with selected feature names
        
       
         
        transformer : transformer instance
            transformer instance (e.g. StandardScaler)
        
       



.. raw:: html

   <h2>Example</h2>

>>> from sam_ml.data.preprocessing import Selector
>>>
>>> model = Selector()
>>> print(model)
Selector()
        

.. raw:: html

   <h2>Methods</h2>

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

   * - Method
     - Description
   
   * - :meth:`~sam_ml.data.preprocessing.feature_selection.Selector.get_params`
     - Function to get the parameter from the transformer instance
   
   * - :meth:`~sam_ml.data.preprocessing.feature_selection.Selector.params`
     - Function to get the possible/recommended parameter values for the class
   
   * - :meth:`~sam_ml.data.preprocessing.feature_selection.Selector.select`
     - Select the best features from data
   
   * - :meth:`~sam_ml.data.preprocessing.feature_selection.Selector.set_params`
     - Function to set the parameter of the transformer instance
   




.. automethod:: sam_ml.data.preprocessing.feature_selection.Selector.get_params

.. automethod:: sam_ml.data.preprocessing.feature_selection.Selector.params

.. automethod:: sam_ml.data.preprocessing.feature_selection.Selector.select

.. automethod:: sam_ml.data.preprocessing.feature_selection.Selector.set_params