cave.analyzer.cave_forward_selection module

class cave.analyzer.cave_forward_selection.CaveForwardSelection(runscontainer, marginal_threshold=0.05)[source]

Bases: cave.analyzer.cave_parameter_importance.CaveParameterImportance

Forward Selection is a generic method to obtain a subset of parameters to achieve the same prediction error as with the full parameter set. Each parameter is scored by how much the out-of-bag-error of an empirical performance model based on a random forest is decreased.

Calculate parameter-importance using the PIMP-package.

classmethod check_for_bokeh(d)
get_html(d=None, tooltip=None)

General reports in html-format, to be easily integrated in html-code. ALSO FOR BOKEH-OUTPUT.

Parameters

d (Dictionary) – a dictionary that will be later turned into a website

Returns

script, div – header and body part of html-code

Return type

str, str

get_jupyter()

Depending on analysis, this creates jupyter-notebook compatible output.

get_name()[source]
parameter_importance(modus)
modus: str

modus for parameter importance, from [forward-selection, ablation, fanova, lpi]

plot_bokeh()

This function needs to be called if bokeh-plots are to be displayed in notebook AND saved to webpage.

plot_whiskers()
postprocess(pimp, output_dir)[source]