optuna.visualization.matplotlib.plot_param_importances(study, evaluator=None, params=None, *, target=None, target_name='Objective Value')[source]

Plot hyperparameter importances with Matplotlib.

See also

Please refer to optuna.visualization.plot_param_importances() for an example.


The following code snippet shows how to plot hyperparameter importances.

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)

  • study (Study) – An optimized study.

  • evaluator (Optional[BaseImportanceEvaluator]) – An importance evaluator object that specifies which algorithm to base the importance assessment on. Defaults to FanovaImportanceEvaluator.

  • params (Optional[List[str]]) – A list of names of parameters to assess. If None, all parameters that are present in all of the completed trials are assessed.

  • target (Optional[Callable[[FrozenTrial], float]]) –

    A function to specify the value to display. If it is None and study is being used for single-objective optimization, the objective values are plotted.


    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 (str) – Target’s name to display on the axis label.


A matplotlib.axes.Axes object.

Return type



Added in v2.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.2.0.