plot_bootstrap

---

method plot_bootstrap(models=None, metric=None, title=None, legend="lower right", figsize=None, filename=None, display=True)[source]

Plot the bootstrapping scores.

If all models applied bootstrap, it shows a boxplot of the results. If only some models applied bootstrap, the plot is a barplot, where the standard deviation of the bootstrapped results is shown as a black line on top of the bar. Models are ordered based on their score from the top down.

Tip

Use the plot_results method to compare the model's scores on any metric.

Parameters

models: int, str, Model, segment, sequence or None, default=None Models to plot. If None, all models are selected. metric: int, str, sequence or None, default=None Metric to plot. Use a sequence or add + between options to select more than one. If None, the metric used to run the pipeline is selected. title: str, dict or None, default=None Title for the plot. If None, no title is shown. If str, text for the title. If dict, title configuration. 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: Position to display the legend. If dict: Legend configuration. figsize: tuple or None, default=None Figure's size in pixels, format as (x, y). If None, it adapts the size to the number of models. filename: str, Path 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.

Returns

go.Figure or None Plot object. Only returned if display=None.

See Also

plot_learning_curve

Plot the learning curve: score vs number of training samples.

plot_results

Compare metric results of the models.

plot_threshold

Plot metric performances against threshold values.

Example

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import make_classification

>>> X, y = make_classification(n_samples=1000, flip_y=0.2, random_state=1)

>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.run(["GNB", "LR"], metric=["f1", "recall"], n_bootstrap=5)
>>> atom.plot_bootstrap()

>>> # Add another model without bootstrap
>>> atom.run("LDA")
>>> atom.plot_bootstrap()