pycaret.clustering.plot_model#

pycaret.clustering.plot_model(model, plot: str = 'cluster', feature: str | None = None, label: bool = False, scale: float = 1, save: bool = False, display_format: str | None = None) → str | None[source]#

This function analyzes the performance of a trained model.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
>>> plot_model(kmeans, plot = 'cluster')
model: scikit-learn compatible object

Trained Model Object

plot: str, default = ‘cluster’

List of available plots (ID - Name):

  • ‘cluster’ - Cluster PCA Plot (2d)

  • ‘tsne’ - Cluster t-SNE (3d)

  • ‘elbow’ - Elbow Plot

  • ‘silhouette’ - Silhouette Plot

  • ‘distance’ - Distance Plot

  • ‘distribution’ - Distribution Plot

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.

label: bool, default = False

Name of column to be used as data labels. Ignored when plot is not ‘cluster’ or ‘tsne’.

scale: float, default = 1

The resolution scale of the figure.

save: bool, default = False

When set to True, plot is saved in the current working directory.

display_format: str, default = None

To display plots in Streamlit (https://www.streamlit.io/), set this to ‘streamlit’. Currently, not all plots are supported.

Returns:

Path to saved file, if any.

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