save model and load model function in regression in pycaret

This recipe explains the save model load model function in regression in Pycaret. The save model function saves the transformation pipeline and the load model function loads a previously saved pipeline.

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Recipe Objective - What are the save_model and load_model functions in the regression model in pycaret?

PyCaret provides the save_model and load_model function in the regression module.

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Table of Contents

Save model function with Example:-

PyCaret provides "pycaret.regression.save_model()" function.

The Save model function saves the transformation pipeline and trained model object into the current working directory as a pickle file for later use.

from pycaret.datasets import get_data
bost = get_data('boston')
# importing regression module
from pycaret.regression import *
# initialize setup
set_up = setup(data = bost, target = 'medv')
# logistic regression
log_reg = create_model('lr')
# saving model
save_model(log_reg, 'saved_lr_model')

load_model function with Example:-

PyCaret provides "pycaret.regression.load_model()" function.

The load model function loads a previously saved pipeline.

from pycaret.regression import load_model
saved_lr = load_model('saved_lr_model')
saved_lr

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