class_docstr: Base class for estimators that use libsvm as backing library
  
  This implements support vector machine classification and regression.
  
  Parameter documentation is in the derived `SVC` class.
class_docstr_tok: ["Base","class","for","estimators","that","use","libsvm","as","backing","library","This","implements","support","vector","machine","classification","and","regression",".","Parameter","documentation","is","in","the","derived","`","SVC","`","class","."]
docstr: Validation of y and class_weight.
  
  Default implementation for SVR and one-class; overridden in BaseSVC.
docstr_tok: ["Validation","of","y","and","class_weight",".","Default","implementation","for","SVR","and","one-class",";","overridden","in","BaseSVC","."]
code: def _validate_targets(self, y):
      """Validation of y and class_weight.
  
      Default implementation for SVR and one-class; overridden in BaseSVC.
      """
      # XXX this is ugly.
      # Regression models should not have a class_weight_ attribute.
      self.class_weight_ = np.empty(0)
      return column_or_1d(y, warn=True).astype(np.float64)
  
imports: from __future__ import print_function
  import numpy
  import scipy.sparse
  import warnings
  from abc import ABCMeta, abstractmethod
  from .None import libsvm, liblinear
  from .None import libsvm_sparse
  from ..base import BaseEstimator, ClassifierMixin
  from ..preprocessing import LabelEncoder
  from ..utils.multiclass import _ovr_decision_function
  from ..utils import check_array, check_consistent_length, check_random_state
  from ..utils import column_or_1d, check_X_y
  from ..utils import compute_class_weight, deprecated
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..externals import six
  from ..exceptions import ChangedBehaviorWarning
  from ..exceptions import ConvergenceWarning
  from ..exceptions import NotFittedError
label_desc: Use image node_id 4 for calling the BaseLibSVM obj's underlying member method code with example usage: obj._validate_targets(y) and returns: column_or_1d