Skip to content

plot_det

method plot_det(models=None, dataset="test", target=0, title=None, legend="upper right", figsize=(900, 600), filename=None, display=True)[source]
Plot the Detection Error Tradeoff curve.

Read more about DET in sklearn's documentation. Only available for binary classification tasks.

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

dataset: str or sequence, default="test"
Data set on which to calculate the metric. Use a sequence or add + between options to select more than one. Choose from: "train", "test" or "holdout".

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: 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_gains

Plot the cumulative gains curve.

plot_roc

Plot the Receiver Operating Characteristics curve.

plot_prc

Plot the precision-recall curve.


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_det()