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plot_shap_scatter

method plot_shap_scatter(models=None, index=None, columns=0, target=1, title=None, legend=None, figsize=(900, 600), filename=None, display=True)[source]
Plot SHAP's scatter plot.

Plots the value of the feature on the x-axis and the SHAP value of the same feature on the y-axis. This shows how the model depends on the given feature, and is like a richer extension of the classical partial dependence plots. Vertical dispersion of the data points represents interaction effects. Read more about SHAP plots in the user guide.

Parametersmodels: int, str, Model or None, default=None
Model to plot. If None, all models are selected. Note that leaving the default option could raise an exception if there are multiple models. To avoid this, call the plot directly from a model, e.g. atom.lr.plot_shap_scatter().

index: slice, sequence or None, default=None
Rows in the dataset to plot. If None, it selects all rows in the test set. The plot_shap_scatter method does not support plotting a single sample.

columns: int or str, default=0
Column to plot.

target: int or str, default=1
Class in the target column to look at (only for multi-class classification tasks).

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

legend: str, dict or None, default=None
Does nothing. Implemented for continuity of the API.

figsize: tuple or None, 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.

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


See Also

plot_shap_beeswarm

Plot SHAP's beeswarm plot.

plot_shap_decision

Plot SHAP's decision plot.

plot_shap_force

Plot SHAP's force plot.


Example

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

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

>>> atom = ATOMClassifier(X, y)
>>> atom.run("LR")
>>> atom.plot_shap_scatter(columns="symmetry error")

plot_shap_scatter