pycaret.regression.predict_model#

pycaret.regression.predict_model(estimator, data: DataFrame | None = None, round: int = 4, verbose: bool = True) → DataFrame[source]#

This function predicts Label using a trained model. When data is None, it predicts label on the holdout set.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> pred_holdout = predict_model(lr)
>>> pred_unseen = predict_model(lr, data = unseen_dataframe)
estimator: scikit-learn compatible object

Trained model object

datapandas.DataFrame

Shape (n_samples, n_features). All features used during training must be available in the unseen dataset.

round: int, default = 4

Number of decimal places to round predictions to.

verbose: bool, default = True

When set to False, holdout score grid is not printed.

Returns:

pandas.DataFrame

Warning

  • The behavior of the predict_model is changed in version 2.1 without backward compatibility. As such, the pipelines trained using the version (<= 2.0), may not work for inference with version >= 2.1. You can either retrain your models with a newer version or downgrade the version for inference.

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