pycaret.clustering.evaluate_model#

pycaret.clustering.evaluate_model(model, feature: str | None = None, fit_kwargs: dict | None = None)[source]#

This function displays a user interface for analyzing performance of a trained model. It calls the plot_model function internally.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
>>> evaluate_model(kmeans)

model: scikit-learn compatible object: Trained model object
feature: str, default = None: Feature to be evaluated when plot = ‘distribution’. When plot type is ‘cluster’ or ‘tsne’ feature column is used as a hoverover tooltip and/or label when the label param is set to True. When the plot type is ‘cluster’ or ‘tsne’ and feature is None, first column of the dataset is used.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.

Returns:: None

Warning

This function only works in IPython enabled Notebook.