Python PyCaret Experiment Logging














































Python PyCaret Experiment Logging




Experiment Logging

 
PyCaret 2.0 embeds MLflow tracking component as a backend API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. Here is how you can log your experiment in PyCaret.

# import classification module
from pycaret.classification import *
# init setup
clf1 = setup(data, target = 'name-of-target', log_experiment = True, experiment_name = 'exp-name-here')
# compare models
best = compare_models()
# start mlflow server on localhost:5000 (when using notebook)
!mlflow ui
view raw

Output (on localhost:5000)

 


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