(
    study: Study,
    evaluator: Optional[BaseImportanceEvaluator] = None,
    params: Optional[List[str]] = None,
    *,
    target: Optional[Callable[[FrozenTrial], float]] = None,
    target_name: str = "Objective Value",
)
return _get_param_importance_plot(study, evaluator, params, target, target_name)
Plot hyperparameter importances with Matplotlib.

    .. seealso::
        Please refer to :func:`optuna.visualization.plot_param_importances` for an example.

    Example:

        The following code snippet shows how to plot hyperparameter importances.

        .. plot::

            import optuna


            def objective(trial):
                x = trial.suggest_int("x", 0, 2)
                y = trial.suggest_float("y", -1.0, 1.0)
                z = trial.suggest_float("z", 0.0, 1.5)
                return x ** 2 + y ** 3 - z ** 4


            sampler = optuna.samplers.RandomSampler(seed=10)
            study = optuna.create_study(sampler=sampler)
            study.optimize(objective, n_trials=100)

            optuna.visualization.matplotlib.plot_param_importances(study)

    Args:
        study:
            An optimized study.
        evaluator:
            An importance evaluator object that specifies which algorithm to base the importance
            assessment on.
            Defaults to
            :class:`~optuna.importance.FanovaImportanceEvaluator`.
        params:
            A list of names of parameters to assess.
            If :obj:`None`, all parameters that are present in all of the completed trials are
            assessed.
        target:
            A function to specify the value to display. If it is :obj:`None` and ``study`` is being
            used for single-objective optimization, the objective values are plotted.

            .. note::
                Specify this argument if ``study`` is being used for multi-objective
                optimization. For example, to get the hyperparameter importance of the first
                objective, use ``target=lambda t: t.values[0]`` for the target parameter.
        target_name:
            Target's name to display on the axis label.

    Returns:
        A :class:`matplotlib.axes.Axes` object.

    Raises:
        :exc:`ValueError`:
            If ``target`` is :obj:`None` and ``study`` is being used for multi-objective
            optimization.
Plot hyperparameter importances with Matplotlib.
Plot hyperparameter importances with Matplotlib .
def plot_param_importances(
    study: Study,
    evaluator: Optional[BaseImportanceEvaluator] = None,
    params: Optional[List[str]] = None,
    *,
    target: Optional[Callable[[FrozenTrial], float]] = None,
    target_name: str = "Objective Value",
) -> "Axes":
    """Plot hyperparameter importances with Matplotlib.

    .. seealso::
        Please refer to :func:`optuna.visualization.plot_param_importances` for an example.

    Example:

        The following code snippet shows how to plot hyperparameter importances.

        .. plot::

            import optuna


            def objective(trial):
                x = trial.suggest_int("x", 0, 2)
                y = trial.suggest_float("y", -1.0, 1.0)
                z = trial.suggest_float("z", 0.0, 1.5)
                return x ** 2 + y ** 3 - z ** 4


            sampler = optuna.samplers.RandomSampler(seed=10)
            study = optuna.create_study(sampler=sampler)
            study.optimize(objective, n_trials=100)

            optuna.visualization.matplotlib.plot_param_importances(study)

    Args:
        study:
            An optimized study.
        evaluator:
            An importance evaluator object that specifies which algorithm to base the importance
            assessment on.
            Defaults to
            :class:`~optuna.importance.FanovaImportanceEvaluator`.
        params:
            A list of names of parameters to assess.
            If :obj:`None`, all parameters that are present in all of the completed trials are
            assessed.
        target:
            A function to specify the value to display. If it is :obj:`None` and ``study`` is being
            used for single-objective optimization, the objective values are plotted.

            .. note::
                Specify this argument if ``study`` is being used for multi-objective
                optimization. For example, to get the hyperparameter importance of the first
                objective, use ``target=lambda t: t.values[0]`` for the target parameter.
        target_name:
            Target's name to display on the axis label.

    Returns:
        A :class:`matplotlib.axes.Axes` object.

    Raises:
        :exc:`ValueError`:
            If ``target`` is :obj:`None` and ``study`` is being used for multi-objective
            optimization.
    """

    _imports.check()
    _check_plot_args(study, target, target_name)
    return _get_param_importance_plot(study, evaluator, params, target, target_name)
def plot_param_importances ( study : Study , evaluator : Optional [ BaseImportanceEvaluator ] = None , params : Optional [ List [ str ] ] = None , * , target : Optional [ Callable [ [ FrozenTrial ] , float ] ] = None , target_name : str = "Objective Value" , ) -> "Axes" : _imports . check ( ) _check_plot_args ( study , target , target_name ) return _get_param_importance_plot ( study , evaluator , params , target , target_name )