parameters: (self, X, raw_score=False, num_iteration=-1,
                  pred_leaf=False, pred_contrib=False, **kwargs)
return_statement: return self.booster_.predict(X, raw_score=raw_score, num_iteration=num_iteration,
                                       pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs)
docstring: Return the predicted value for each sample.
  
          Parameters
          ----------
          X : array-like or sparse matrix of shape = [n_samples, n_features]
              Input features matrix.
          raw_score : bool, optional (default=False)
              Whether to predict raw scores.
          num_iteration : int, optional (default=-1)
              Limit number of iterations in the prediction.
              If <= 0, uses all trees (no limits).
          pred_leaf : bool, optional (default=False)
              Whether to predict leaf index.
          pred_contrib : bool, optional (default=False)
              Whether to predict feature contributions.
          **kwargs : other parameters for the prediction
  
          Returns
          -------
          predicted_result : array-like of shape = [n_samples] or shape = [n_samples, n_classes]
              The predicted values.
          X_leaves : array-like of shape = [n_samples, n_trees] or shape [n_samples, n_trees * n_classes]
              If ``pred_leaf=True``, the predicted leaf every tree for each sample.
          X_SHAP_values : array-like of shape = [n_samples, n_features + 1] or shape [n_samples, (n_features + 1) * n_classes]
              If ``pred_contrib=True``, the each feature contributions for each sample.
docstring_summary: Return the predicted value for each sample.
docstring_tokens: Return the predicted value for each sample .
function: def predict(self, X, raw_score=False, num_iteration=-1,
                  pred_leaf=False, pred_contrib=False, **kwargs):
          """Return the predicted value for each sample.
  
          Parameters
          ----------
          X : array-like or sparse matrix of shape = [n_samples, n_features]
              Input features matrix.
          raw_score : bool, optional (default=False)
              Whether to predict raw scores.
          num_iteration : int, optional (default=-1)
              Limit number of iterations in the prediction.
              If <= 0, uses all trees (no limits).
          pred_leaf : bool, optional (default=False)
              Whether to predict leaf index.
          pred_contrib : bool, optional (default=False)
              Whether to predict feature contributions.
          **kwargs : other parameters for the prediction
  
          Returns
          -------
          predicted_result : array-like of shape = [n_samples] or shape = [n_samples, n_classes]
              The predicted values.
          X_leaves : array-like of shape = [n_samples, n_trees] or shape [n_samples, n_trees * n_classes]
              If ``pred_leaf=True``, the predicted leaf every tree for each sample.
          X_SHAP_values : array-like of shape = [n_samples, n_features + 1] or shape [n_samples, (n_features + 1) * n_classes]
              If ``pred_contrib=True``, the each feature contributions for each sample.
          """
          if self._n_features is None:
              raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.")
          if not isinstance(X, DataFrame):
              X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
          n_features = X.shape[1]
          if self._n_features != n_features:
              raise ValueError("Number of features of the model must "
                               "match the input. Model n_features_ is %s and "
                               "input n_features is %s "
                               % (self._n_features, n_features))
          return self.booster_.predict(X, raw_score=raw_score, num_iteration=num_iteration,
                                       pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs)
function_tokens: def predict ( self , X , raw_score = False , num_iteration = - 1 , pred_leaf = False , pred_contrib = False , * * kwargs ) : if self . _n_features is None : raise LGBMNotFittedError ( "Estimator not fitted, call `fit` before exploiting the model." ) if not isinstance ( X , DataFrame ) : X = _LGBMCheckArray ( X , accept_sparse = True , force_all_finite = False ) n_features = X . shape [ 1 ] if self . _n_features != n_features : raise ValueError ( "Number of features of the model must " "match the input. Model n_features_ is %s and " "input n_features is %s " % ( self . _n_features , n_features ) ) return self . booster_ . predict ( X , raw_score = raw_score , num_iteration = num_iteration , pred_leaf = pred_leaf , pred_contrib = pred_contrib , * * kwargs )