Explain models get metrics and add metric functions in PyCaret
In this recipe, we will see what are models, get metrics and add metric functions in the classification model in PyCaret through examples.
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
Table of Contents
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
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)
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