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plot_errors

method plot_errors(models=None, dataset="test", target=0, title=None, legend="lower right", figsize=(900, 600), filename=None, display=True)[source]
Plot a model's prediction errors.

Plot the actual targets from a set against the predicted values generated by the regressor. A linear fit is made on the data. The gray, intersected line shows the identity line. This plot can be useful to detect noise or heteroscedasticity along a range of the target domain. This plot is available only for regression 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=0
Target column to look at. Only for multioutput tasks.

title: str, dict or None, default=None
Title for the plot.

legend: str, dict or None, default="lower 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_residuals

Plot a model's residuals.


Example

>>> from atom import ATOMRegressor
>>> from sklearn.datasets import load_diabetes

>>> X, y = load_diabetes(return_X_y=True, as_frame=True)

>>> atom = ATOMRegressor(X, y)
>>> atom.run(["OLS", "LGB"])
>>> atom.plot_errors()