semvec/pypi-packages huggingface dataset:
neptune-sklearn 2.1.4
Neptune.ai scikit-learn integration library
License: Apache-2.0
Requires Python: <4.0,>=3.7
Keywords: MLOps, ML Experiment Tracking, ML Model Registry, ML Model Store, ML Metadata Store

# Neptune + scikit-learn integration

Experiment tracking for scikit-learn&ndash;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](https://docs.neptune.ai/logging/what_you_can_log)

![image](https://docs.neptune.ai/img/app/integrations/scikit-learn.png)

## Resources

* [Documentation](https://docs.neptune.ai/integrations/sklearn)
* [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/sklearn/scripts/Neptune_Scikit_learn_classification.py)
* [Runs logged in the Neptune app](https://app.neptune.ai/o/common/org/sklearn-integration/e/SKLEAR-95/all)
* [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/master/integrations-and-supported-tools/sklearn/notebooks/Neptune_Scikit_learn.ipynb)

## Example

```
# On the command line:
pip install neptune-sklearn
```

```python
# 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](https://docs.neptune.ai/getting_help)
* 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