# 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](https://docs.neptune.ai/logging/what_you_can_log)
## 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
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"description": "# Neptune + scikit-learn integration\n\nExperiment tracking for scikit-learn–trained models.\n\n## What will you get with this integration?\n\n* Log, organize, visualize, and compare ML experiments in a single place\n* Monitor model training live\n* Version and query production-ready models and associated metadata (e.g., datasets)\n* Collaborate with the team and across the organization\n\n## What will be logged to Neptune?\n\n* classifier and regressor parameters,\n* pickled model,\n* test predictions,\n* test predictions probabilities,\n* test scores,\n* classifier and regressor visualizations, like confusion matrix, precision-recall chart, and feature importance chart,\n* KMeans cluster labels and clustering visualizations,\n* metadata including git summary info,\n* [other metadata](https://docs.neptune.ai/logging/what_you_can_log)\n\n\n\n## Resources\n\n* [Documentation](https://docs.neptune.ai/integrations/sklearn)\n* [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/sklearn/scripts/Neptune_Scikit_learn_classification.py)\n* [Runs logged in the Neptune app](https://app.neptune.ai/o/common/org/sklearn-integration/e/SKLEAR-95/all)\n* [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)\n\n## Example\n\n```\n# On the command line:\npip install neptune-sklearn\n```\n\n```python\n# In Python, prepare a fitted estimator\nparameters = {\n \"n_estimators\": 70, \"max_depth\": 7, \"min_samples_split\": 3\n}\n\nestimator = ...\nestimator.fit(X_train, y_train)\n\n# Import Neptune and start a run\nimport neptune\n\nrun = neptune.init_run(\n project=\"common/sklearn-integration\",\n api_token=neptune.ANONYMOUS_API_TOKEN,\n)\n\n# Log parameters and scores\nrun[\"parameters\"] = parameters\n\ny_pred = estimator.predict(X_test)\n\nrun[\"scores/max_error\"] = max_error(y_test, y_pred)\nrun[\"scores/mean_absolute_error\"] = mean_absolute_error(y_test, y_pred)\nrun[\"scores/r2_score\"] = r2_score(y_test, y_pred)\n\n# Stop the run\nrun.stop()\n```\n\n## Support\n\nIf you got stuck or simply want to talk to us, here are your options:\n\n* Check our [FAQ page](https://docs.neptune.ai/getting_help)\n* You can submit bug reports, feature requests, or contributions directly to the repository.\n* 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),\n* You can just shoot us an email at support@neptune.ai\n\n",
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