class_docstr: fANOVA importance evaluator.
  
  Implements the fANOVA hyperparameter importance evaluation algorithm in
  `An Efficient Approach for Assessing Hyperparameter Importance
  <http://proceedings.mlr.press/v32/hutter14.html>`_.
  
  fANOVA fits a random forest regression model that predicts the objective values
  of :class:`~optuna.trial.TrialState.COMPLETE` trials given their parameter configurations.
  The more accurate this model is, the more reliable the importances assessed
  by this class are.
  
  .. note::
  
      This class takes over 1 minute when given a study that contains 1000+ trials.
      We published `optuna-fast-fanova <https://github.com/optuna/optuna-fast-fanova>`_ library,
      that is a Cython accelerated fANOVA implementation. By using it, you can get hyperparameter
      importances within a few seconds.
  
  .. note::
  
      Requires the `sklearn <https://github.com/scikit-learn/scikit-learn>`_ Python package.
  
  .. note::
  
      The performance of fANOVA depends on the prediction performance of the underlying
      random forest model. In order to obtain high prediction performance, it is necessary to
      cover a wide range of the hyperparameter search space. It is recommended to use an
      exploration-oriented sampler such as :class:`~optuna.samplers.RandomSampler`.
  
  .. note::
  
      For how to cite the original work, please refer to
      https://automl.github.io/fanova/cite.html.
  
  Args:
      n_trees:
          The number of trees in the forest.
      max_depth:
          The maximum depth of the trees in the forest.
      seed:
          Controls the randomness of the forest. For deterministic behavior, specify a value
          other than :obj:`None`.
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code: def __init__(
      self,
      *,
      n_trees: int = 64,
      max_depth: int = 64,
      seed: Optional[int] = None,
  ) -> None:
      self._evaluator = _Fanova(
          n_trees=n_trees,
          max_depth=max_depth,
          min_samples_split=2,
          min_samples_leaf=1,
          seed=seed,
      )
  
imports: from typing import Callable
  from typing import Dict
  from typing import List
  from typing import Optional
  import numpy
  from optuna._transform import _SearchSpaceTransform
  from optuna.importance._base import _get_distributions
  from optuna.importance._base import _get_filtered_trials
  from optuna.importance._base import _get_target_values
  from optuna.importance._base import _get_trans_params
  from optuna.importance._base import _param_importances_to_dict
  from optuna.importance._base import _sort_dict_by_importance
  from optuna.importance._base import BaseImportanceEvaluator
  from optuna.importance._fanova._fanova import _Fanova
  from optuna.study import Study
  from optuna.trial import FrozenTrial
label_desc: Use image node_id 1 to create a new FanovaImportanceEvaluator object from inherited base classes: BaseImportanceEvaluator with example: obj = FanovaImportanceEvaluator()