lightgbm.plot_metric¶
-
lightgbm.
plot_metric
(booster, metric=None, dataset_names=None, ax=None, xlim=None, ylim=None, title='Metric during training', xlabel='Iterations', ylabel='auto', figsize=None, dpi=None, grid=True)[source]¶ Plot one metric during training.
- Parameters
booster (dict or LGBMModel) – Dictionary returned from
lightgbm.train()
or LGBMModel instance.metric (string or None, optional (default=None)) – The metric name to plot. Only one metric supported because different metrics have various scales. If None, first metric picked from dictionary (according to hashcode).
dataset_names (list of strings or None, optional (default=None)) – List of the dataset names which are used to calculate metric to plot. If None, all datasets are used.
ax (matplotlib.axes.Axes or None, optional (default=None)) – Target axes instance. If None, new figure and axes will be created.
xlim (tuple of 2 elements or None, optional (default=None)) – Tuple passed to
ax.xlim()
.ylim (tuple of 2 elements or None, optional (default=None)) – Tuple passed to
ax.ylim()
.title (string or None, optional (default="Metric during training")) – Axes title. If None, title is disabled.
xlabel (string or None, optional (default="Iterations")) – X-axis title label. If None, title is disabled.
ylabel (string or None, optional (default="auto")) – Y-axis title label. If ‘auto’, metric name is used. If None, title is disabled.
figsize (tuple of 2 elements or None, optional (default=None)) – Figure size.
dpi (int or None, optional (default=None)) – Resolution of the figure.
grid (bool, optional (default=True)) – Whether to add a grid for axes.
- Returns
ax – The plot with metric’s history over the training.
- Return type
matplotlib.axes.Axes