Both pdpbox.info_plots.TargetPlot and pdpbox.info_plots.PredictPlot inherit from pdpbox.info_plots._InfoPlot class and share the same plot method.

class pdpbox.info_plots._InfoPlot(df, feature, feature_name, target=None, model=None, model_features=None, pred_func=None, n_classes=None, predict_kwds=None, chunk_size=-1, plot_type='target', **kwargs)

Methods

plot([which_classes, show_percentile, ...])

The plot function for TargetPlot and PredictPlot.

plot(which_classes=None, show_percentile=False, figsize=None, dpi=300, ncols=2, plot_params=None, engine='plotly', template='plotly_white')

The plot function for TargetPlot and PredictPlot.

Parameters:

which_classeslist of int, optional

List of class indices to plot. If None, all classes will be plotted. Default is None.

show_percentilebool, optional

If True, percentiles are shown in the plot. Default is False.

figsizetuple or None, optional

The figure size for matplotlib or plotly figure. If None, the default figure size is used. Default is None.

dpiint, optional

The resolution of the plot, measured in dots per inch. Only applicable when engine is ‘matplotlib’. Default is 300.

ncolsint, optional

The number of columns of subplots in the figure. Default is 2.

plot_paramsdict or None, optional

Custom plot parameters that control the style and aesthetics of the plot. Default is None.

engine{‘matplotlib’, ‘plotly’}, optional

The plotting engine to use. Default is plotly.

templatestr, optional

The template to use for plotly plots. Only applicable when engine is ‘plotly’. Reference: https://plotly.com/python/templates/ Default is plotly_white.

Returns:

matplotlib.figure.Figure or plotly.graph_objects.Figure

A Matplotlib or Plotly figure object depending on the plot engine being used.

dict of matplotlib.axes.Axes or None

A dictionary of Matplotlib axes objects. The keys are the names of the axes. The values are the axes objects. If engine is ‘ploltly’, it is None.

pd.DataFrame

A DataFrame that contains the summary statistics of target (for target plot) or predict (for predict plot) values for each feature bucket.