plot_prc

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

Plot the Precision-Recall Curve (PRC). The legend shows the average precision (AP) score. Only for binary classification tasks. Read more about PRC in sklearn's documentation.

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 metric. 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 ATOMClassifier

atom = ATOMClassifier(X, y)
atom.run(["LR", "RF", "LGB"], metric="average_precision")
atom.plot_prc()