parameters: (self, X, missing_values_bin_idx)
return_statement: return out
docstring: Predict raw values for binned data.
  
          Parameters
          ----------
          X : ndarray, shape (n_samples, n_features)
              The input samples.
          missing_values_bin_idx : uint8
              Index of the bin that is used for missing values. This is the
              index of the last bin and is always equal to max_bins (as passed
              to the GBDT classes), or equivalently to n_bins - 1.
  
          Returns
          -------
          y : ndarray, shape (n_samples,)
              The raw predicted values.
docstring_summary: Predict raw values for binned data.
docstring_tokens: Predict raw values for binned data .
function: def predict_binned(self, X, missing_values_bin_idx):
          """Predict raw values for binned data.
  
          Parameters
          ----------
          X : ndarray, shape (n_samples, n_features)
              The input samples.
          missing_values_bin_idx : uint8
              Index of the bin that is used for missing values. This is the
              index of the last bin and is always equal to max_bins (as passed
              to the GBDT classes), or equivalently to n_bins - 1.
  
          Returns
          -------
          y : ndarray, shape (n_samples,)
              The raw predicted values.
          """
          out = np.empty(X.shape[0], dtype=Y_DTYPE)
          _predict_from_binned_data(self.nodes, X, missing_values_bin_idx, out)
          return out
function_tokens: def predict_binned ( self , X , missing_values_bin_idx ) : out = np . empty ( X . shape [ 0 ] , dtype = Y_DTYPE ) _predict_from_binned_data ( self . nodes , X , missing_values_bin_idx , out ) return out