class_docstr: LinearSVR (Support Vector Regression with the linear kernel)
  
  Construct a linear SVM regressor for training and predictions.
  
  Examples
  --------
  .. code-block:: python
  
      >>> import cupy as cp
      >>> from cuml.svm import LinearSVR
      >>> X = cp.array([[1], [2], [3], [4], [5]], dtype=cp.float32)
      >>> y = cp.array([1.1, 4, 5, 3.9, 8.], dtype=cp.float32)
      >>> reg = LinearSVR(loss='epsilon_insensitive', C=10,
      ...                 epsilon=0.1, verbose=0)
      >>> reg.fit(X, y)
      LinearSVR()
      >>> print("Predicted values:", reg.predict(X)) # doctest: +SKIP
      Predicted labels: [1.8993504 3.3995128 4.899675  6.399837  7.899999]
  
  Parameters
  ----------
  handle : cuml.Handle
      Specifies the cuml.handle that holds internal CUDA state for
      computations in this model. Most importantly, this specifies the CUDA
      stream that will be used for the model's computations, so users can
      run different models concurrently in different streams by creating
      handles in several streams.
      If it is None, a new one is created.
  penalty : {{'l1', 'l2'}} (default = '{LinearSVM_defaults.penalty}')
      The regularization term of the target function.
  loss : {LinearSVR.REGISTERED_LOSSES} (default = 'epsilon_insensitive')
      The loss term of the target function.
  fit_intercept : {LinearSVM_defaults.fit_intercept.__class__.__name__             } (default = {LinearSVM_defaults.fit_intercept})
      Whether to fit the bias term. Set to False if you expect that the
      data is already centered.
  penalized_intercept : {             LinearSVM_defaults.penalized_intercept.__class__.__name__             } (default = {LinearSVM_defaults.penalized_intercept})
      When true, the bias term is treated the same way as other features;
      i.e. it's penalized by the regularization term of the target function.
      Enabling this feature forces an extra copying the input data X.
  max_iter : {LinearSVM_defaults.max_iter.__class__.__name__             } (default = {LinearSVM_defaults.max_iter})
      Maximum number of iterations for the underlying solver.
  linesearch_max_iter : {             LinearSVM_defaults.linesearch_max_iter.__class__.__name__             } (default = {LinearSVM_defaults.linesearch_max_iter})
      Maximum number of linesearch (inner loop) iterations for
      the underlying (QN) solver.
  lbfgs_memory : {             LinearSVM_defaults.lbfgs_memory.__class__.__name__             } (default = {LinearSVM_defaults.lbfgs_memory})
      Number of vectors approximating the hessian for the underlying QN
      solver (l-bfgs).
  verbose : int or boolean, default=False
      Sets logging level. It must be one of `cuml.common.logger.level_*`.
      See :ref:`verbosity-levels` for more info.
  C : {LinearSVM_defaults.C.__class__.__name__             } (default = {LinearSVM_defaults.C})
      The constant scaling factor of the loss term in the target formula
        `F(X, y) = penalty(X) + C * loss(X, y)`.
  grad_tol : {LinearSVM_defaults.grad_tol.__class__.__name__             } (default = {LinearSVM_defaults.grad_tol})
      The threshold on the gradient for the underlying QN solver.
  change_tol : {LinearSVM_defaults.change_tol.__class__.__name__             } (default = {LinearSVM_defaults.change_tol})
      The threshold on the function change for the underlying QN solver.
  tol : Optional[float] (default = None)
      Tolerance for the stopping criterion.
      This is a helper transient parameter that, when present, sets both
      `grad_tol` and `change_tol` to the same value. When any of the two
      `***_tol` parameters are passed as well, they take the precedence.
  epsilon : {LinearSVM_defaults.epsilon.__class__.__name__             } (default = {LinearSVM_defaults.epsilon})
      The epsilon-sensitivity parameter for the SVR loss function.
  output_type : {{'input', 'array', 'dataframe', 'series', 'df_obj',         'numba', 'cupy', 'numpy', 'cudf', 'pandas'}}, default=None
      Return results and set estimator attributes to the indicated output
      type. If None, the output type set at the module level
      (`cuml.global_settings.output_type`) will be used. See
      :ref:`output-data-type-configuration` for more info.
  
  Attributes
  ----------
  intercept_ : float, shape (1,)
      The constant in the decision function
  coef_ : float, shape (1, n_cols)
      The coefficients of the linear decision function.
  
  Notes
  -----
  The model uses the quasi-newton (QN) solver to find the solution in the
  primal space. Thus, in contrast to generic :class:`SVC<cuml.svm.SVR>`
  model, it does not compute the support coefficients/vectors.
  
  Check the solver's documentation for more details
  :class:`Quasi-Newton (L-BFGS/OWL-QN)<cuml.QN>`.
  
  For additional docs, see `scikitlearn's LinearSVR
  <https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html>`_.
code: def loss(self, loss: str):
      if loss not in self.REGISTERED_LOSSES:
          raise ValueError(
              f"Regression loss type "
              f"must be one of {self.REGISTERED_LOSSES}, "
              f"but given '{loss}'."
          )
      self.__loss = loss
  
file_path: cuml/python/cuml/svm/linear_svr.py
imports: from cuml.internals.mixins import RegressorMixin
  from cuml.svm.linear import LinearSVM, LinearSVM_defaults
label_desc: Use image node_id 3 for calling the LinearSVR obj's underlying member method code with example usage: obj.loss(loss) without return types