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Integrate with PyCaret

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that PyCaret that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient.

Open In Colab

Start logging

Select Comet as your Pycaret logger to log all of your experiments to your Comet account.

# load dataset
from pycaret.datasets import get_data

data = get_data("diabetes")

# init setup
from pycaret.classification import *

clf1 = (
    setup(
        data,
        target="Class variable",
        log_experiment="comet_ml",
        experiment_name="pycaret_example",
        log_data=True,
    ),
)

# model training
best_model = compare_models()

Log automatically

The PyCaret integration automatically logs the following information to Comet:

  • Metrics
  • Hyperparameters
  • Models
  • CSV Files

Compare the Performance of Different Algorithms with Comet's Panels

When you use Comet with PyCaret, it's easier to compare different algorithms and find the best one for what you need. Comet's visualizations can help you do this faster.

For example, Comet's bar chart can be used to compare algorithm performance across different metrics:

pycaret_bar_chart.png

Comet's data panel can also be used to visualize model benchmarks. The compare.csv file is logged by the initial experiment run (the experiment with a setup tag):

pycaret_data_panel.png

Apr. 19, 2024