Save Model


Saving a trained model in PyCaret is as simple as writing save_model. The function takes a trained model object and saves the entire transformation pipeline and trained model object as a transferable binary pickle file for later use.

 

Example

 

Code
# Importing dataset
from pycaret.datasets import get_data
diabetes = get_data('diabetes')

# Importing module and initializing setup
from pycaret.classification import *
clf1 = setup(data = diabetes, target = 'Class variable')

# create a model
dt = create_model('dt')

# save a model
save_model(dt, 'dt_saved_07032020')

 

Output

Loading saved model

 

Code
# Loading the saved model
dt_saved = load_model('dt_saved_07032020')

 

Output

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