Predict model, finalize model, deploy model functions in PyCaret
In this recipe, we will see what are predict model finalize model and deploy model functions in the classification model in PyCaret through examples.
Recipe Objective - What are predict_model, finalize_model, and deploy_model functions in the classification model in PyCaret?
PyCaret provides predict_model, finalize_model, and deploy_model functions in the classification module.
List of Classification Algorithms in Machine Learning
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Predict Model function with Example:
PyCaret provides "pycaret.classification.predict_model()" function. Predict model function predicts "Label" and "Score" using a trained model.
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')
pred_holdout = predict_model(log_reg)
pred_holdout
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')
pred_holdout = predict_model(log_reg)
pred_holdout
finalize_model function with Example:
PyCaret provides "pycaret.classification.finalize_model()" function. Finalize model function trains a given estimator on the entire dataset including.
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')
final_log_reg = finalize_model(log_reg)
final_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')
final_log_reg = finalize_model(log_reg)
final_log_reg
deploy_model function with Example:
PyCaret provides "pycaret.classification.deploy_model()" function. Deploy model function deploys the transformation pipeline and trained model on cloud.
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')
# sets appropriate credentials for the platform as environment variables
import os
# set your cloud ACCESS_KEY_ID, SECRET_ACCESS_KEY
os.environ["AWS_ACCESS_KEY_ID"] = str("foo")
os.environ["AWS_SECRET_ACCESS_KEY"] = str("bar")
deploy_model(model = lr, model_name = 'lr-for-deployment', platform = 'aws',
authentication = {'bucket' : 'S3-bucket-name'})
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')
# sets appropriate credentials for the platform as environment variables
import os
# set your cloud ACCESS_KEY_ID, SECRET_ACCESS_KEY
os.environ["AWS_ACCESS_KEY_ID"] = str("foo")
os.environ["AWS_SECRET_ACCESS_KEY"] = str("bar")
deploy_model(model = lr, model_name = 'lr-for-deployment', platform = 'aws',
authentication = {'bucket' : 'S3-bucket-name'})
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