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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.

Parametersmodels: 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.

  • If None: No legend is shown.
  • If str: Position to display the legend.
  • If dict: Legend configuration.

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 filename has no file type, the plot is saved as html. If None, the plot is not saved.

display: bool or None, default=True
Whether to render the plot. If None, it returns the figure.

Returnsgo.Figure or None
Plot object. Only returned if display=None.


See Also

plot_confusion_matrix

Plot a model's confusion matrix.

plot_results

Compare metric results of the models.

plot_threshold

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()