Explain models get metrics and add metric functions in PyCaret

Recipe Objective - What are models, get_metrics, and add_metric functions in the classification model in PyCaret?

PyCaret provides models, get_metrics, and add_metric functions in the classification module.

List of Classification Algorithms in Machine Learning

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models function with Example:

PyCaret provides "pycaret.classification.models()" function. Models function returns table of models available in the model library.

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')
models = models()
models


get_metrics function with Example:

PyCaret provides "pycaret.classification.get_metrics()" function. The get_metrics function returns table of available metrics used for CV.

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')
get_models = get_metrics()
get_models


add_metric function with Example:

PyCaret provides "pycaret.classification.add_metric()" function. The add_metric function adds a custom metric to be used for CV.

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')
from sklearn.metrics import log_loss
add_metric('logloss', 'Log Loss', log_loss, greater_is_better = False)