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.

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:

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):

Nov. 18, 2024