Explain setup compare models and create model func in pycaret

In this recipe, we shall learn about setup, compare models and create model functions in the classification model in PyCaret with the help of examples.

Recipe Objective - What are the setup, compare_models, and create_model functions in the classification model in Pycaret?

PyCaret provides setup, compare_models, and create_model functions in the classification module.

Learn How to use XLNet for Text Classification

Links for the more related projects:

https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
https://www.dezyre.com/projects/data-science-projects/neural-network-projects

Setup function with Example:

PyCaret provides "pycaret.classification.setup()" function. Setup function initializes the training environment and creates the transformation pipeline.

from pycaret.datasets import get_data
juice = get_data('juice')
# importing classification module
from pycaret.classification import *
# initialize the setup
setup_name = setup(data = juice, target = 'Purchase')

compare_models function with Example:

PyCaret provides "pycaret.classification.compare_models()" function. The compare_model function trains and evaluates performance of all estimators in the model library.

The output of this function is a scoring grid with average cross-validated scores.

from pycaret.datasets import get_data
juice = get_data('juice')
# importing classification module
from pycaret.classification import *
# initialize the compare model
exp_name = setup(data = juice, target = 'Purchase')
comp_model = compare_models()

create_model function with Example:

PyCaret provides "pycaret.classification.create_model()" function. The create_model function trains and evaluates the performance of 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 *
# setup create model
setup_data = setup(data = iris, target = 'species')
# logistic regression
log_reg = create_model('lr')
log_reg

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