plot_successive_halving
method plot_successive_halving(models=None, metric=None, title=None, legend="lower right", figsize=(900, 600), filename=None, display=True)[source]
Plot scores per iteration of the successive halving.
Only use with models fitted using successive halving. Ensembles are ignored.
Parameters |
models: int, str, Model, segment, sequence or None, default=None
Models to plot. If None, all models are selected.
metric: int, str, sequence or None, default=None
Metric to plot (only for multi-metric runs). Use a sequence
or add
title: str, dict or None, default=None+ between options to select more than one. If None,
the metric used to run the pipeline is selected.
Title for the plot.
legend: str, dict or None, default="lower right"
Legend for the plot. See the user guide for
an extended description of the choices.
figsize: tuple, default=(900, 600)
Figure's size in pixels, format as (x, y).
filename: str, Path or None, default=None
Save the plot using this name. Use "auto" for automatic
naming. The type of the file depends on the provided name
(.html, .png, .pdf, etc...). If
display: bool or None, default=Truefilename has no file type,
the plot is saved as html. If None, the plot is not saved.
Whether to render the plot. If None, it returns the figure.
|
Returns | {#plot_successive_halving-go.Figure or None}
go.Figure or None
Plot object. Only returned if display=None .
|
See Also
Plot the learning curve: score vs number of training samples.
Compare metric results of the models.
Example
>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer
>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)
>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.successive_halving(["Tree", "Bag", "RF", "LGB"], n_bootstrap=5)
>>> atom.plot_successive_halving()