compare_classifiers.ensemble_predict
====================================

.. py:module:: compare_classifiers.ensemble_predict


Attributes
----------

.. autoapisummary::

   compare_classifiers.ensemble_predict.METHOD_ERROR


Functions
---------

.. autoapisummary::

   compare_classifiers.ensemble_predict.ensemble_predict


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

.. py:data:: METHOD_ERROR
   :value: 'fourth parameter has to be a string of two possible values: "voting" and "stacking"'


.. py:function:: ensemble_predict(estimators, X_train, y_train, ensemble_method, test_data)

   predict class for test data with provided estimators and whether predicting through Voting or Stacking

   :param estimators: A list of (name, estimator) tuples, consisting of individual estimators to be processed through the voting or stacking classifying ensemble. Each tuple contains a string: name/label of estimator, and a model: the estimator, which implements
                      the scikit-learn API (`fit`, `predict`, etc.).
   :type estimators: list of tuples
   :param X_train: Data frame containing training data along with n features or ndarray with no feature names.
   :type X_train: Pandas data frame or Numpy array
   :param y_train: Target class labels for data in X_train.
   :type y_train: Pandas series or Numpy array
   :param ensemble_method: Whether prediction is made through voting or stacking. Possible values are: 'voting' or 'stacking'.
   :type ensemble_method: str
   :param test_data: Data to make predictions on.
   :type test_data: Pandas data frame

   :returns: Predicted class labels for test_data.
   :rtype: Numpy array

   .. rubric:: Examples

   >>> estimators = [
   ...     ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
   ...     ('svm', make_pipeline(StandardScaler(), LinearSVC(random_state=42)))
   ... ]
   >>> ensemble_predict(estimators, X, y, unseen_data, 'voting')