:mod:`summit.multiview_platform.monoview_classifiers.lasso`
===========================================================

.. py:module:: summit.multiview_platform.monoview_classifiers.lasso


lasso
-----


.. py:data:: classifier_class_name
   :annotation: = Lasso

   

.. py:class:: Lasso(random_state=None, alpha=1.0, max_iter=10, warm_start=False, **kwargs)



   This class is an adaptation of scikit-learn's `Lasso <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html>`_



   .. py:method:: fit(self, X, y, check_input=True)

      Fit model with coordinate descent.

      :param X: Data.
      :type X: {ndarray, sparse matrix} of (n_samples, n_features)
      :param y: Target. Will be cast to X's dtype if necessary.
      :type y: {ndarray, sparse matrix} of shape (n_samples,) or             (n_samples, n_targets)
      :param sample_weight: Sample weights. Internally, the `sample_weight` vector will be
                            rescaled to sum to `n_samples`.

                            .. versionadded:: 0.23
      :type sample_weight: float or array-like of shape (n_samples,), default=None
      :param check_input: Allow to bypass several input checking.
                          Don't use this parameter unless you know what you do.
      :type check_input: bool, default=True

      :returns: **self** -- Fitted estimator.
      :rtype: object

      .. rubric:: Notes

      Coordinate descent is an algorithm that considers each column of
      data at a time hence it will automatically convert the X input
      as a Fortran-contiguous numpy array if necessary.

      To avoid memory re-allocation it is advised to allocate the
      initial data in memory directly using that format.


   .. py:method:: predict(self, X)

      Predict using the linear model.

      :param X: Samples.
      :type X: array-like or sparse matrix, shape (n_samples, n_features)

      :returns: **C** -- Returns predicted values.
      :rtype: array, shape (n_samples,)



