tune model and ensemble model function in regression in pycaret

This recipe explains the tune model and ensemble model function in regression in Pycaret. The tune model function tunes the hyperparameters of a given estimator and the ensemble model function ensembles a given estimator.

Recipe Objective - What are the tune_model and ensemble_model functions in the regression model in pycaret?

PyCaret provides tune_model and ensemble_model functions in the regression module.

A Gentle Introduction to Ensemble Learning in Machine Learning

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Tune Model function with Example:-

PyCaret provides "pycaret.regression.tune_model()" function. The Tune model function tunes the hyper-parameters of a given estimator.

from pycaret.datasets import get_data
bost = get_data('boston')
# importing regression module
from pycaret.regression import *
# initialize setup
setup_name = setup(data = bost, target = 'medv')
# logistic regression
log_reg = create_model('lr')
tuned_log_reg = tune_model(log_reg)
tuned_log_reg

ensemble_model function with Example:-

PyCaret provides "pycaret.regression.ensemble_models()" function. The Ensemble model function ensembles a given estimator.

The output of this function is a scoring grid with CV scores by fold.

from pycaret.datasets import get_data
bost = get_data('boston')
# importing regression module
from pycaret.regression import *
# initialize setup
setup_name = setup(data = bost, target = 'medv')
# SVM model
svm = create_model('svm')
bagged_svm = ensemble_model(svm, method = 'Bagging')
bagged_svm

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