plot_learning_curve

---

method plot_learning_curve(models=None, metric=0, title=None, figsize=(10, 6), filename=None, display=True) [source]

Plot the model's learning curve: score vs number of training samples. Only use with models fitted using train sizing. Ensemble models are ignored.

Parameters:

models: str, sequence or None, optional (default=None) Name of the models to plot. If None, all models in the pipeline are selected. metric: int or str, optional (default=0) Index or name of the metric to plot. Only for multi-metric runs. title: str or None, optional (default=None) Plot's title. If None, the title is left empty. figsize: tuple, optional (default=(10, 6)) Figure's size, format as (x, y). filename: str or None, optional (default=None) Name of the file. Use "auto" for automatic naming. If None, the figure is not saved. display: bool or None, optional (default=True) Whether to render the plot. If None, it returns the matplotlib figure.

Returns:

fig: matplotlib.figure.Figure Plot object. Only returned if display=None.

Example

from atom import ATOMClassifier

atom = ATOMClassifier(X, y)
atom.train_sizing(
    models=["LDA", "RF"],
    metric="precision",
    train_sizes=10,
    n_bootstrap=4,
)
atom.plot_learning_curve()