What are tune ensemble and blend models functions in PyCaret
In this recipe, we shall learn about the tune model, ensemble model and blend models functions in the classification model in PyCaret.
Recipe Objective - What are the tune_model, ensemble_model, and blend_model functions in the classification model in PyCaret?
PyCaret provides tune_model, ensemble_model and blend_models functions in the classification module.
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Tune Model function with Example:
PyCaret provides "pycaret.classification.tune_model()" function. Tune model function tunes the hyperparameters of a given estimator.
from pycaret.datasets import get_data
iris = get_data('iris')
# importing classification module
from pycaret.classification import *
# initialize setup
setup_name = setup(data = iris, target = 'species')
# logistic regression
log_reg = create_model('lr')
tuned_log_reg = tune_model(log_reg)
tuned_log_reg
iris = get_data('iris')
# importing classification module
from pycaret.classification import *
# initialize setup
setup_name = setup(data = iris, target = 'species')
# 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.classification.ensemble_models()" function. 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
iris = get_data('iris')
# importing classification module
from pycaret.classification import *
# initialize setup
setup_name = setup(data = iris, target = 'species')
# SVM model
svm = create_model('svm')
bagged_svm = ensemble_model(svm, method = 'Bagging')
bagged_svm
iris = get_data('iris')
# importing classification module
from pycaret.classification import *
# initialize setup
setup_name = setup(data = iris, target = 'species')
# SVM model
svm = create_model('svm')
bagged_svm = ensemble_model(svm, method = 'Bagging')
bagged_svm
blend_models function with Example:
PyCaret provides "pycaret.classification.blend_models()" function. blend_models function trains a Soft Voting / Majority Rule classifier for select models passed in the "estimator_list" parameters.
The output of this function is a scoring grid with CV scores by fold.
from pycaret.datasets import get_data
iris = get_data('iris')
# importing classification module
from pycaret.classification import *
# initialize setup
setup_name = setup(data = iris, target = 'species')
top3 = compare_models(n_select = 3)
blender = blend_models(top3)
blender
iris = get_data('iris')
# importing classification module
from pycaret.classification import *
# initialize setup
setup_name = setup(data = iris, target = 'species')
top3 = compare_models(n_select = 3)
blender = blend_models(top3)
blender
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