Neptune + scikit-learn integration

Experiment tracking for scikit-learn–trained models.

What will you get with this integration?

• Log, organize, visualize, and compare ML experiments in a single place
• Monitor model training live
• Version and query production-ready models and associated metadata (e.g., datasets)
• Collaborate with the team and across the organization

What will be logged to Neptune?

• classifier and regressor parameters,
• pickled model,
• test predictions,
• test predictions probabilities,
• test scores,
• classifier and regressor visualizations, like confusion matrix, precision-recall chart, and feature importance chart,
• KMeans cluster labels and clustering visualizations,
• metadata including git summary info,
• other metadata

Resources

• Documentation
• Code example on GitHub
• Runs logged in the Neptune app
• Run example in Google Colab

Example

# On the command line:
pip install neptune-sklearn

# In Python, prepare a fitted estimator
parameters = {
    "n_estimators": 70, "max_depth": 7, "min_samples_split": 3
}

estimator = ...
estimator.fit(X_train, y_train)

# Import Neptune and start a run
import neptune

run = neptune.init_run(
    project="common/sklearn-integration",
    api_token=neptune.ANONYMOUS_API_TOKEN,
)

# Log parameters and scores
run["parameters"] = parameters

y_pred = estimator.predict(X_test)

run["scores/max_error"] = max_error(y_test, y_pred)
run["scores/mean_absolute_error"] = mean_absolute_error(y_test, y_pred)
run["scores/r2_score"] = r2_score(y_test, y_pred)

# Stop the run
run.stop()

Support

If you got stuck or simply want to talk to us, here are your options:

• Check our FAQ page
• You can submit bug reports, feature requests, or contributions directly to the repository.
• Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),
• You can just shoot us an email at [email protected]