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plot_probabilities

method plot_probabilities(models=None, dataset="test", target=1, title=None, legend="upper right", figsize=(900, 600), filename=None, display=True)[source]
Plot the probability distribution of the target classes.

Only available for classification tasks.

Parametersmodels: int, str, Model, slice, sequence or None, default=None
Models to plot. If None, all models are selected.

dataset: str, default="test"
Data set on which to calculate the metric. Choose from: "train", "test" or "holdout".

target: int or str, default=1
Probability of being that class in the target column (only for multiclass classification tasks).

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: Location where to show the legend.
  • If dict: Legend configuration.

figsize: tuple, default=(900, 600)
Figure's size in pixels, format as (x, y).

filename: str 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

Plot the model results.

plot_threshold

Plot metric performances against threshold values.


Example

>>> from atom import ATOMClassifier
>>> import pandas as pd

>>> X = pd.read_csv("./examples/datasets/weatherAUS.csv")

>>> atom = ATOMClassifier(X, y="RainTomorrow", n_rows=1e4)
>>> atom.impute()
>>> atom.encode()
>>> atom.run(["LR", "RF"])
>>> atom.plot_probabilities()