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plot_lift

method plot_lift(models=None, rows="test", target=0, title=None, legend="upper right", figsize=(900, 600), filename=None, display=True)[source]
Plot the lift curve.

Only available for binary classification tasks.

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

rows: str, sequence or dict, default="test"
Selection of rows on which to calculate the metric.

  • If str: Name of the data set to plot.
  • If sequence: Names of the data sets to plot.
  • If dict: Names of the sets with corresponding selection of rows as values.

target: int or str, default=0
Target column to look at. Only for multilabel 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: 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_det

Plot the Detection Error Tradeoff curve.

plot_gains

Plot the cumulative gains curve.

plot_prc

Plot the precision-recall curve.


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_lift()