pycaret.anomaly.assign_model#

pycaret.anomaly.assign_model(model, transformation: bool = False, score: bool = True, verbose: bool = True) → DataFrame[source]#

This function assigns anomaly labels to the dataset for a given model. (1 = outlier, 0 = inlier).

Example

>>> from pycaret.datasets import get_data
>>> anomaly = get_data('anomaly')
>>> from pycaret.anomaly import *
>>> exp_name = setup(data = anomaly)
>>> knn = create_model('knn')
>>> knn_df = assign_model(knn)

model: scikit-learn compatible object: Trained model object
transformation: bool, default = False: Whether to apply anomaly labels on the transformed dataset.
score: bool, default = True: Whether to show outlier score or not.
verbose: bool, default = True: Status update is not printed when verbose is set to False.

Returns:: pandas.DataFrame