set config and save config function in regression model in pycaret
This recipe explains the set config save config function in the regression model in pycaret. The set config function resets the global variables and the save config function saves all global variables to a pickle file.
Recipe Objective - What is the set_config, save_config function in the regression model in pycaret?
PyCaret provides the set_config and save_config function in the regression module.
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Table of Contents
For more related projects:-
https://www.dezyre.com/projects/data-science-projects/tensorflow-projects
https://www.dezyre.com/projects/data-science-projects/keras-deep-learning-projects
set_config function with Example:-
PyCaret provides "pycaret.regression.set_config()" function. The set_config function resets the global variables.
Some Accessible variables:-
X: Transformed dataset (X)
y: Transformed dataset (y)
X_train: Transformed train dataset (X)
X_test: Transformed test/holdout dataset (X)
y_train: Transformed train dataset (y)
y_test: Transformed test/holdout dataset (y)
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')
set_config('seed', 123)
save_config function with Example:-
PyCaret provides "pycaret.regression.save_config()" function.
The save_config function saves all global variables to a pickle file, allowing to resume without rerunning the setup later.
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')
# saving our model
save_config('my_pycaret_model2.pkl')
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