pycaret.regression.dashboard#

pycaret.regression.dashboard(estimator, display_format: str = 'dash', dashboard_kwargs: Dict[str, Any] | None = None, run_kwargs: Dict[str, Any] | None = None, **kwargs)[source]#

This function generates the interactive dashboard for a trained model. The dashboard is implemented using ExplainerDashboard (explainerdashboard.readthedocs.io)

Example

>>> from pycaret.datasets import get_data
>>> insurance = get_data('insurance')
>>> from pycaret.regression import *
>>> exp_name = setup(data = insurance,  target = 'charges')
>>> lr = create_model('lr')
>>> dashboard(lr)
estimator: scikit-learn compatible object

Trained model object

display_format: str, default = ‘dash’

Render mode for the dashboard. The default is set to dash which will render a dashboard in browser. There are four possible options:

  • ‘dash’ - displays the dashboard in browser

  • ‘inline’ - displays the dashboard in the jupyter notebook cell.

  • ‘jupyterlab’ - displays the dashboard in jupyterlab pane.

  • ‘external’ - displays the dashboard in a separate tab. (use in Colab)

dashboard_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the ExplainerDashboard class.

run_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the run method of ExplainerDashboard.

**kwargs:

Additional keyword arguments to pass to the ClassifierExplainer or RegressionExplainer class.

Returns:

ExplainerDashboard

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