optuna.visualization.plot_param_importances(study: optuna.study.Study, evaluator: optuna.importance._base.BaseImportanceEvaluator = None, params: Optional[List[str]] = None) → go.Figure[source]

Plot hyperparameter importances.


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

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


See also

This function visualizes the results of optuna.importance.get_param_importances().

  • study – An optimized study.

  • evaluator – An importance evaluator object that specifies which algorithm to base the importance assessment on. Defaults to MeanDecreaseImpurityImportanceEvaluator.

  • params – A list of names of parameters to assess. If None, all parameters that are present in all of the completed trials are assessed.


A plotly.graph_objs.Figure object.


Added in v1.5.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.5.0.