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: Evaluates the decision function for the samples in X.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
  
  Returns
  -------
  X : array-like of shape (n_samples, n_class * (n_class-1) / 2)
      Returns the decision function of the sample for each class
      in the model.
code: def _decision_function(self, X):
      """Evaluates the decision function for the samples in X.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
  
      Returns
      -------
      X : array-like of shape (n_samples, n_class * (n_class-1) / 2)
          Returns the decision function of the sample for each class
          in the model.
      """
      # NOTE: _validate_for_predict contains check for is_fitted
      # hence must be placed before any other attributes are used.
      X = self._validate_for_predict(X)
      X = self._compute_kernel(X)
  
      if self._sparse:
          dec_func = self._sparse_decision_function(X)
      else:
          dec_func = self._dense_decision_function(X)
  
      # In binary case, we need to flip the sign of coef, intercept and
      # decision function.
      if self._impl in ["c_svc", "nu_svc"] and len(self.classes_) == 2:
          return -dec_func.ravel()
  
      return dec_func
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, ClassifierMixin, _fit_context
  from ..exceptions import ConvergenceWarning, NotFittedError
  from ..preprocessing import LabelEncoder
  from ..utils import check_array, check_random_state, column_or_1d, compute_class_weight
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import safe_sparse_dot
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import _ovr_decision_function, check_classification_targets
  from ..utils.validation import _check_large_sparse, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
  from .None import _liblinear
  from .None import _libsvm
  from .None import _libsvm_sparse
label_desc: Use image node_id 12 for calling the BaseLibSVM obj's underlying member method code with example usage: obj._decision_function(X) and returns: dec_func, unknown