optuna.visualization.plot_optimization_history(study, *, target=None, target_name='Objective Value', error_bar=False)[source]

Plot optimization history of all trials in a study.


The following code snippet shows how to plot optimization history.

import optuna

def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_categorical("y", [-1, 0, 1])
    return x ** 2 + y

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

fig = optuna.visualization.plot_optimization_history(study)
  • study (Study | Sequence[Study]) – A Study object whose trials are plotted for their target values. You can pass multiple studies if you want to compare those optimization histories.

  • target (Callable[[FrozenTrial], float] | None) –

    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.

  • target_name (str) – Target’s name to display on the axis label and the legend.

  • error_bar (bool) – A flag to show the error bar.


A plotly.graph_objects.Figure object.

Return type: