parameters: (self, X, y, sample_weight=None)
return_statement: return super(AdaBoostClassifier, self).fit(X, y, sample_weight)
docstring: Build a boosted classifier from the training set (X, y).
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape = [n_samples, n_features]
              The training input samples. Sparse matrix can be CSC, CSR, COO,
              DOK, or LIL. DOK and LIL are converted to CSR.
  
          y : array-like of shape = [n_samples]
              The target values (class labels).
  
          sample_weight : array-like of shape = [n_samples], optional
              Sample weights. If None, the sample weights are initialized to
              ``1 / n_samples``.
  
          Returns
          -------
          self : object
              Returns self.
docstring_summary: Build a boosted classifier from the training set (X, y).
docstring_tokens: Build a boosted classifier from the training set ( X y ) .
function: def fit(self, X, y, sample_weight=None):
          """Build a boosted classifier from the training set (X, y).
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape = [n_samples, n_features]
              The training input samples. Sparse matrix can be CSC, CSR, COO,
              DOK, or LIL. DOK and LIL are converted to CSR.
  
          y : array-like of shape = [n_samples]
              The target values (class labels).
  
          sample_weight : array-like of shape = [n_samples], optional
              Sample weights. If None, the sample weights are initialized to
              ``1 / n_samples``.
  
          Returns
          -------
          self : object
              Returns self.
          """
          # Check that algorithm is supported
          if self.algorithm not in ('SAMME', 'SAMME.R'):
              raise ValueError("algorithm %s is not supported" % self.algorithm)
  
          # Fit
          return super(AdaBoostClassifier, self).fit(X, y, sample_weight)
function_tokens: def fit ( self , X , y , sample_weight = None ) : # Check that algorithm is supported if self . algorithm not in ( 'SAMME' , 'SAMME.R' ) : raise ValueError ( "algorithm %s is not supported" % self . algorithm ) # Fit return super ( AdaBoostClassifier , self ) . fit ( X , y , sample_weight )