decision_plot

---

method decision_plot(models=None, index=None, show=None, target=1, title=None, figsize=None, filename=None, display=True, **kwargs) [source]

Plot SHAP's decision plot. Visualize model decisions using cumulative SHAP values. Each plotted line explains a single model prediction. If a single prediction is plotted, feature values will be printed in the plot (if supplied). If multiple predictions are plotted together, feature values will not be printed. Plotting too many predictions together will make the plot unintelligible. Read more about SHAP plots in the user guide.

Parameters:

models: str, sequence or None, optional (default=None) Name of the model to plot. If None, all models in the pipeline are selected. Note that leaving the default option could raise an exception if there are multiple models in the pipeline. To avoid this, call the plot from a model, e.g. atom.xgb.decision_plot(). index: int, tuple, slice or None, optional (default=None) Indices of the rows in the dataset to plot. If tuple (n, m), it selects rows n until m. If None, select all rows in the test set. show: int or None, optional (default=None) Number of features (ordered by importance) to show. None to show all. target: int or str, optional (default=1) Index or name of the class in the target column to look at. Only for multi-class classification tasks. title: str or None, optional (default=None) Plot's title. If None, the title is left empty. figsize: tuple or None, optional (default=None) Figure's size, format as (x, y). If None, it adapts the size to the number of features shown. 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. **kwargs Additional keyword arguments for SHAP's decision plot.

Returns:

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

Example

from atom import ATOMRegressor

atom = ATOMRegressor(X, y)
atom.run("RF")
atom.decision_plot()  # For multiple samples

atom.decision_plot(index=120)  # For a single sample