pycaret.anomaly.plot_model#

pycaret.anomaly.plot_model(model, plot: str = 'tsne', 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
>>> anomaly = get_data('anomaly')
>>> from pycaret.anomaly import *
>>> exp_name = setup(data = anomaly)
>>> knn = create_model('knn')
>>> plot_model(knn, plot = 'tsne')
model: scikit-learn compatible object

Trained Model Object

plot: str, default = ‘tsne’

List of available plots (ID - Name):

  • ‘tsne’ - t-SNE (3d) Dimension Plot

  • ‘umap’ - UMAP Dimensionality Plot

feature: str, default = None

Feature to be used as a hoverover tooltip and/or label when the label param is set to True. When feature is None, first column of the dataset is used.

label: bool, default = False

Name of column to be used as data labels.

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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