:mod:`summit.multiview_platform.multiview_classifiers.svm_jumbo_fusion`
=======================================================================

.. py:module:: summit.multiview_platform.multiview_classifiers.svm_jumbo_fusion


svm_jumbo_fusion
----------------


.. py:data:: classifier_class_name
   :value: 'SVMJumboFusion'


.. py:class:: SVMJumboFusion(random_state=None, classifiers_names=None, classifier_configs=None, nb_cores=1, weights=None, nb_monoview_per_view=1, C=1.0, kernel='rbf', degree=2, rs=None)



   This classifier learns monoview classifiers on each view and then uses an
   SVM on their decisions to aggregate them.


   .. py:attribute:: need_probas
      :value: False



   .. py:attribute:: aggregation_estimator


   .. py:attribute:: C
      :value: 1.0



   .. py:attribute:: kernel
      :value: 'rbf'



   .. py:attribute:: degree
      :value: 2



   .. py:method:: set_params(C=1.0, kernel='rbf', degree=1, **params)

      Set the parameters of this estimator.

      The method works on simple estimators as well as on nested objects
      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
      parameters of the form ``<component>__<parameter>`` so that it's
      possible to update each component of a nested object.

      :param \*\*params: Estimator parameters.
      :type \*\*params: dict

      :returns: **self** -- Estimator instance.
      :rtype: estimator instance



