plot_errors

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method plot_errors(models=None, dataset="test", title=None, figsize=(10, 6), filename=None, display=True) [source]

Plot a model's prediction errors, i.e. 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 pot can be useful to detect noise or heteroscedasticity along a range of the target domain. Only for regression tasks.

Parameters:

models: str, sequence or None, optional (default=None) Name of the models to plot. If None, all models in the pipeline are selected. dataset: str, optional (default="test") Data set on which to calculate the errors. Choose from: "train", "test", "both" (train and test) or "holdout". title: str or None, optional (default=None) Plot's title. If None, the title is left empty. figsize: tuple, optional (default=(10, 6)) Figure's size, format as (x, y). filename: str or None, optional (default=None) Name of the file. Use "auto" for automatic naming. If None, the figure is not saved. display: bool or None, optional (default=True) Whether to render the plot. If None, it returns the matplotlib figure.

Returns:

matplotlib.figure.Figure Plot object. Only returned if display=None.

Example

from atom import ATOMRegressor

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