class_docstr: Base class for histogram-based gradient boosting estimators.
class_docstr_tok: ["Base","class","for","histogram-based","gradient","boosting","estimators","."]
docstr: Add the predictions of the predictors to raw_predictions.
docstr_tok: ["Add","the","predictions","of","the","predictors","to","raw_predictions","."]
code: def _predict_iterations(
      self, X, predictors, raw_predictions, is_binned, n_threads
  ):
      """Add the predictions of the predictors to raw_predictions."""
      if not is_binned:
          (
              known_cat_bitsets,
              f_idx_map,
          ) = self._bin_mapper.make_known_categories_bitsets()
  
      for predictors_of_ith_iteration in predictors:
          for k, predictor in enumerate(predictors_of_ith_iteration):
              if is_binned:
                  predict = partial(
                      predictor.predict_binned,
                      missing_values_bin_idx=self._bin_mapper.missing_values_bin_idx_,
                      n_threads=n_threads,
                  )
              else:
                  predict = partial(
                      predictor.predict,
                      known_cat_bitsets=known_cat_bitsets,
                      f_idx_map=f_idx_map,
                      n_threads=n_threads,
                  )
              raw_predictions[:, k] += predict(X)
  
imports: import itertools
  from abc import ABC, abstractmethod
  from functools import partial
  from numbers import Integral, Real
  from timeit import default_timer
  import numpy
  from ..._loss.loss import _LOSSES, BaseLoss, HalfBinomialLoss, HalfGammaLoss, HalfMultinomialLoss, HalfPoissonLoss, PinballLoss
  from ...base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ...metrics import check_scoring
  from ...model_selection import train_test_split
  from ...preprocessing import LabelEncoder
  from ...utils import check_random_state, compute_sample_weight, resample
  from ...utils._openmp_helpers import _openmp_effective_n_threads
  from ...utils._param_validation import Interval, RealNotInt, StrOptions
  from ...utils.multiclass import check_classification_targets
  from ...utils.validation import _check_monotonic_cst, _check_sample_weight, check_consistent_length, check_is_fitted
  from ._gradient_boosting import _update_raw_predictions
  from .binning import _BinMapper
  from .common import G_H_DTYPE, X_DTYPE, Y_DTYPE
  from .grower import TreeGrower
label_desc: Use image node_id 16 for calling the BaseHistGradientBoosting obj's underlying member method code with example usage: obj._predict_iterations(X, predictors, raw_predictions, is_binned, n_threads) without return types