Reduced run time on ML with ODC notebook

[erialC-P]

Proposed changes

This notebook is unchanged except for a down-sampling of the input training data polygons by 50 % to improve the notebook run time by the same amount.

Checklist

(Replace [ ] with [x] to check off)

• [x ] Notebook created using the DEA-notebooks template
• Remove any unused Python packages from Load packages
• [x ] Remove any unused/empty code cells
• [x ] Remove any guidance cells (e.g. General advice)
• [ x] Ensure that all code cells follow the PEP8 standard for code. The jupyterlab_code_formatter tool can be used to format code cells to a consistent style: select each code cell, then click Edit and then one of the Apply X Formatter options (YAPF or Black are recommended).
• [x ] Include relevant tags in the final notebook cell (refer to the DEA Tags Index, and re-use tags if possible)
• [x ] Clear all outputs, run notebook from start to finish, and save the notebook in the state where all cells have been sequentially evaluated
• Test notebook on both the NCI and DEA Sandbox (flag if not working as part of PR and ask for help to solve if needed)
• If applicable, update the Notebook currently compatible with the NCI|DEA Sandbox environment only line below the notebook title to reflect the environments the notebook is compatible with
• [x ] Check for any spelling mistakes using the DEA Sandbox's built-in spellchecker (double click on markdown cells then right-click on pink highlighted words). For example:

[review-notebook-app]

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[BexDunn]

Looks ok to me :) Not super familiar with the underlying notebook, but looks faster and as if it still does things :)

[BexDunn]

Oops, meant to review this last time instead of just commenting

[robbibt]

Awesome!