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

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 support@neptune.ai