plot_probabilities
method plot_probabilities(models=None, rows="test", target=1, title=None, legend="upper right", figsize=(900, 600), filename=None, display=True)[source]
Plot the probability distribution of the target classes.
This plot is available only for models with a predict_proba
method in classification tasks.
Parameters |
models: int, str, Model, segment, sequence or None, default=None
Models to plot. If None, all models are selected.
rows: hashable, segment or sequence, default="test"
Selection of rows on which to
calculate the metric.
target: int, str or tuple, default=1
Probability of being that class in the target column. For
multioutput tasks, the value should be a tuple of the form
(column, class).
title: str, dict or None, default=None
Title for the plot.
legend: str, dict or None, default="upper 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_probabilities-go.Figure or None}
go.Figure or None
Plot object. Only returned if display=None .
|
See Also
Plot a model's confusion matrix.
Compare metric results of the models.
Plot metric performances against threshold values.
Example
>>> from atom import ATOMClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, flip_y=0.2, random_state=1)
>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.run(["LR", "RF"])
>>> atom.plot_probabilities()