parameters: (self, data, start_iteration=0, num_iteration=None,
                  raw_score=False, pred_leaf=False, pred_contrib=False,
                  data_has_header=False, is_reshape=True, **kwargs)
return_statement: return predictor.predict(data, start_iteration, num_iteration,
                                   raw_score, pred_leaf, pred_contrib,
                                   data_has_header, is_reshape)
docstring: Make a prediction.
  
          Parameters
          ----------
          data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
              Data source for prediction.
              If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).
          start_iteration : int, optional (default=0)
              Start index of the iteration to predict.
              If <= 0, starts from the first iteration.
          num_iteration : int or None, optional (default=None)
              Total number of iterations used in the prediction.
              If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;
              otherwise, all iterations from ``start_iteration`` are used (no limits).
              If <= 0, all iterations from ``start_iteration`` are used (no limits).
          raw_score : bool, optional (default=False)
              Whether to predict raw scores.
          pred_leaf : bool, optional (default=False)
              Whether to predict leaf index.
          pred_contrib : bool, optional (default=False)
              Whether to predict feature contributions.
  
              .. note::
  
                  If you want to get more explanations for your model's predictions using SHAP values,
                  like SHAP interaction values,
                  you can install the shap package (https://github.com/slundberg/shap).
                  Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra
                  column, where the last column is the expected value.
  
          data_has_header : bool, optional (default=False)
              Whether the data has header.
              Used only if data is str.
          is_reshape : bool, optional (default=True)
              If True, result is reshaped to [nrow, ncol].
          **kwargs
              Other parameters for the prediction.
  
          Returns
          -------
          result : numpy array, scipy.sparse or list of scipy.sparse
              Prediction result.
              Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``).
docstring_summary: Make a prediction.
docstring_tokens: Make a prediction .
function: def predict(self, data, start_iteration=0, num_iteration=None,
                  raw_score=False, pred_leaf=False, pred_contrib=False,
                  data_has_header=False, is_reshape=True, **kwargs):
          """Make a prediction.
  
          Parameters
          ----------
          data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
              Data source for prediction.
              If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).
          start_iteration : int, optional (default=0)
              Start index of the iteration to predict.
              If <= 0, starts from the first iteration.
          num_iteration : int or None, optional (default=None)
              Total number of iterations used in the prediction.
              If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;
              otherwise, all iterations from ``start_iteration`` are used (no limits).
              If <= 0, all iterations from ``start_iteration`` are used (no limits).
          raw_score : bool, optional (default=False)
              Whether to predict raw scores.
          pred_leaf : bool, optional (default=False)
              Whether to predict leaf index.
          pred_contrib : bool, optional (default=False)
              Whether to predict feature contributions.
  
              .. note::
  
                  If you want to get more explanations for your model's predictions using SHAP values,
                  like SHAP interaction values,
                  you can install the shap package (https://github.com/slundberg/shap).
                  Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra
                  column, where the last column is the expected value.
  
          data_has_header : bool, optional (default=False)
              Whether the data has header.
              Used only if data is str.
          is_reshape : bool, optional (default=True)
              If True, result is reshaped to [nrow, ncol].
          **kwargs
              Other parameters for the prediction.
  
          Returns
          -------
          result : numpy array, scipy.sparse or list of scipy.sparse
              Prediction result.
              Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``).
          """
          predictor = self._to_predictor(deepcopy(kwargs))
          if num_iteration is None:
              if start_iteration <= 0:
                  num_iteration = self.best_iteration
              else:
                  num_iteration = -1
          return predictor.predict(data, start_iteration, num_iteration,
                                   raw_score, pred_leaf, pred_contrib,
                                   data_has_header, is_reshape)
function_tokens: def predict ( self , data , start_iteration = 0 , num_iteration = None , raw_score = False , pred_leaf = False , pred_contrib = False , data_has_header = False , is_reshape = True , * * kwargs ) : predictor = self . _to_predictor ( deepcopy ( kwargs ) ) if num_iteration is None : if start_iteration <= 0 : num_iteration = self . best_iteration else : num_iteration = - 1 return predictor . predict ( data , start_iteration , num_iteration , raw_score , pred_leaf , pred_contrib , data_has_header , is_reshape )