scikit-learn instead of sklearn

Error observed in mlflow-v2-examples in notebook-example.ipynb and pipeline-example.ipynb. There is an import of sklearn which causes an error. This is blocking the MLflow tutorial.

Workaround: if you replace sklearn with scikit-learn and run the cell even once it fixes the error globally (a side effect of running pip install commands from notebooks - they can affect the entire notebook server).

Error log:

Collecting sklearn
  Downloading sklearn-0.0.post5.tar.gz (3.7 kB)
  Preparing metadata (setup.py) ... error
  error: subprocess-exited-with-error

  × python setup.py egg\_info did not run successfully.
  │ exit code: 1
  ╰─> \[18 lines of output\]
   The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
   rather than 'sklearn' for pip commands.

   Here is how to fix this error in the main use cases:
   - use 'pip install scikit-learn' rather than 'pip install sklearn'
   - replace 'sklearn' by 'scikit-learn' in your pip requirements files
     (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
   - if the 'sklearn' package is used by one of your dependencies,
     it would be great if you take some time to track which package uses
     'sklearn' instead of 'scikit-learn' and report it to their issue tracker
   - as a last resort, set the environment variable
     SKLEARN\_ALLOW\_DEPRECATED\_SKLEARN\_PACKAGE\_INSTALL=True to avoid this error

   More information is available at
   [https://github.com/scikit-learn/sklearn-pypi-package](https://github.com/scikit-learn/sklearn-pypi-package)

   If the previous advice does not cover your use case, feel free to report it at
   [https://github.com/scikit-learn/sklearn-pypi-package/issues/new](https://github.com/scikit-learn/sklearn-pypi-package/issues/new)
   \[end of output\]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

canonical / kubeflow-examples

scikit-learn instead of sklearn #34