plot_threshold

---

method plot_threshold(models=None, metric=None, dataset="test", steps=100, title=None, figsize=(10, 6), filename=None, display=True) [source]

Plot metric performances against threshold values. Only for binary classification 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. metric: str, func, scorer, sequence or None, optional (default=None) Metric to plot. Choose from any of sklearn's SCORERS, a function with signature metric(y_true, y_pred), a scorer object or a sequence of these. If None, the metric used to run the pipeline is plotted. dataset: str, optional (default="test") Data set on which to calculate the metric. Options are "train", "test" or "both". steps: int, optional (default=100) Number of thresholds measured. 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:

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

Example

from atom import ATOMClassifier
from sklearn.metrics import recall_score

atom = ATOMClassifier(X, y)
atom.run("LGB")
atom.plot_threshold(metric=["accuracy", "f1", recall_score])