class_docstr: This class is an abstract model of a committee-based active learning regression.
  Args:
      learner_list: A list of ActiveLearners forming the CommitteeRegressor.
      query_strategy: Query strategy function.
      on_transformed: Whether to transform samples with the pipeline defined by each learner's estimator
          when applying the query strategy.
  Examples:
      >>> import numpy as np
      >>> import matplotlib.pyplot as plt
      >>> from sklearn.gaussian_process import GaussianProcessRegressor
      >>> from sklearn.gaussian_process.kernels import WhiteKernel, RBF
      >>> from modAL.models import ActiveLearner, CommitteeRegressor
      >>>
      >>> # generating the data
      >>> X = np.concatenate((np.random.rand(100)-1, np.random.rand(100)))
      >>> y = np.abs(X) + np.random.normal(scale=0.2, size=X.shape)
      >>>
      >>> # initializing the regressors
      >>> n_initial = 10
      >>> kernel = RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e3)) + WhiteKernel(noise_level=1, noise_level_bounds=(1e-10, 1e+1))
      >>>
      >>> initial_idx = list()
      >>> initial_idx.append(np.random.choice(range(100), size=n_initial, replace=False))
      >>> initial_idx.append(np.random.choice(range(100, 200), size=n_initial, replace=False))
      >>> learner_list = [ActiveLearner(
      ...                         estimator=GaussianProcessRegressor(kernel),
      ...                         X_training=X[idx].reshape(-1, 1), y_training=y[idx].reshape(-1, 1)
      ...                 )
      ...                 for idx in initial_idx]
      >>>
      >>> # query strategy for regression
      >>> def ensemble_regression_std(regressor, X):
      ...     _, std = regressor.predict(X, return_std=True)
      ...     return np.argmax(std)
      >>>
      >>> # initializing the CommitteeRegressor
      >>> committee = CommitteeRegressor(
      ...     learner_list=learner_list,
      ...     query_strategy=ensemble_regression_std
      ... )
      >>>
      >>> # active regression
      >>> n_queries = 10
      >>> for idx in range(n_queries):
      ...     query_idx, query_instance = committee.query(X.reshape(-1, 1))
      ...     committee.teach(X[query_idx].reshape(-1, 1), y[query_idx].reshape(-1, 1))
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docstr: Predicts the values of the samples by averaging the prediction of each regressor.
  Args:
      X: The samples to be predicted.
      **predict_kwargs: Keyword arguments to be passed to the :meth:`vote` method of the CommitteeRegressor.
  Returns:
      The predicted class labels for X.
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code: def predict(
      self, X: modALinput, return_std: bool = False, **predict_kwargs
  ) -> Any:
      """
      Predicts the values of the samples by averaging the prediction of each regressor.
      Args:
          X: The samples to be predicted.
          **predict_kwargs: Keyword arguments to be passed to the :meth:`vote` method of the CommitteeRegressor.
      Returns:
          The predicted class labels for X.
      """
      vote = self.vote(X, **predict_kwargs)
      if not return_std:
          return np.mean(vote, axis=1)
      else:
          return np.mean(vote, axis=1), np.std(vote, axis=1)
  
imports: from typing import Any, Callable, List, Optional, Tuple
  import numpy
  from modAL.acquisition import max_EI
  from modAL.disagreement import max_std_sampling, vote_entropy_sampling
  from modAL.models.base import BaseCommittee, BaseLearner
  from modAL.uncertainty import uncertainty_sampling
  from modAL.utils.data import data_vstack, modALinput, retrieve_rows
  from modAL.utils.validation import check_class_labels, check_class_proba
  from sklearn.base import BaseEstimator
  from sklearn.metrics import accuracy_score
  from sklearn.utils import check_X_y
label_desc: Use image node_id 2 for calling the CommitteeRegressor obj's underlying member method code with example usage: obj.predict(X, return_std) and returns: np, np,  np