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

Metadata

Release files for neptune-sklearn 2.1.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for neptune-sklearn 2.1.4
File	Size	Uploaded	Provenance
neptune_sklearn-2.1.4.tar.gz	14.6 kB	Jul 5, 2024	Without provenance	Details

Built distribution (wheel)

Table of built distributions (wheels) for neptune-sklearn 2.1.4
File	Interpreter	ABI	Platform	Reset
neptune_sklearn-2.1.4-py3-none-any.whl 15.0 kB Jul 5, 2024 Without provenance	Python 3	none	any	Details

Total release size: 29.6 kB

Release files / neptune_sklearn-2.1.4.tar.gz

Download URL	neptune_sklearn-2.1.4.tar.gz
Size	14.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`9687a4cc8349e5378766c48fe014ccaf955e155c40f9cc2fd38ebf0fd50a4fd7`
BLAKE2b-256 checksum How to use checksums	`9ba5a7f5aa08f481ef03acd1cc296536a4f0cf1c14a8508c992d7dbb597665a7`
Upload date	Jul 5, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.9.19`

Release files / neptune_sklearn-2.1.4-py3-none-any.whl

Download URL	neptune_sklearn-2.1.4-py3-none-any.whl
Size	15.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`f0fce972ecf38e801a51aa611b226d00f9a16f0b192c0382b1751d3bccb0ff3f`
BLAKE2b-256 checksum How to use checksums	`8c63eb829b258168b99d5932353102be0be245a27ead2995c60933cc0b1d171e`
Upload date	Jul 5, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.9.19`

Security for neptune-sklearn 2.1.4

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