plot_cumlift

scikitplot.modelplotpy.plot_cumlift(plot_input, *, highlight_ntile=None, highlight_how='plot_text', save_fig=True, save_fig_filename='', **kwargs)

Plotting cumulative lift curve

Parameters:

plot_inputpandas.DataFrame

The result from scope_modevalplot().

highlight_ntileint or None, optional, default=None

Highlight the value of the response curve at a specified ntile value.

Changed in version 0.3.9: Default changed from False to None.

highlight_how{‘plot’,’text’,’plot_text’}, optional, default=’plot_text’

Highlight_how specifies where information about the model performance is printed. It can be shown as text, on the plot or both.

**kwargsdict, optional

Generic keyword arguments.

Returns:

matplotlib.axes._subplots.AxesSubplot

It returns a matplotlib.axes._subplots.AxesSubplot object that can be transformed into the same plot with the .figure command. The plot is by default written to disk (save_fig = True). The location and filetype of the file depend on the save_fig_filename parameter. If the save_fig_filename parameter is empty (not specified), the plot will be written to the working directory as png. Otherwise the location and file type is specified by the user.

Other Parameters:

show_figbool, default=True

Show the plot.

save_figbool, default=False

Save the plot.

save_fig_filenamestr, optional, default=’’

Specify the path and filetype to save the plot. If nothing specified, the plot will be saved as png inside result_images under to the current working directory. Defaults to plot image named to used func.__name__.

verbosebool, optional

If True, prints debugging information.

Raises:

TypeError

If highlight_ntile is not specified as an int.

ValueError

If the wrong highlight_how value is specified.

Parameters:

plot_input (pandas.DataFrame)

Gallery examples

Introduction to modelplotpy

Introduction to modelplotpy