[TESTING] html widgets in notebooks

[validbeck]

Pull Request Description

What and why?

Testing the html widgets update from validmind/validmind-library#458 (correct test PR this time...)

How to test

What needs special review?

Dependencies, breaking changes, and deployment notes

Release notes

Checklist

• What and why
• Screenshots or videos (Frontend)
• How to test
• What needs special review
• Dependencies, breaking changes, and deployment notes
• Labels applied
• PR linked to Shortcut
• Unit tests added (Backend)
• Tested locally
• Documentation updated (if required)
• Environment variable additions/changes documented (if required)

[github-actions]

Lighthouse check results

⚠️ WARN: Average accessibility score is 0.87 (required: >0.9) — Check the workflow run

Show Lighthouse scores

Folder depth level checked: 0

Commit SHA: 6c4b8c6

Modify the workflow to check a different depth:

• 0: Top-level navigation only — /index.html, /guide/guides.html, ...
• 1: All first-level subdirectories — /guide/.html, /developer/.html, ...
• 2: All second-level subdirectories — /guide/attestation/*.html, ...

Page	Accessibility	Performance	Best Practices	SEO
/developer/validmind-library.html	0.85	0.36	1.00	0.90
/get-started/get-started.html	0.85	0.69	1.00	0.80
/guide/guides.html	0.85	0.68	1.00	0.90
/index.html	0.93	0.66	1.00	0.90
/releases/all-releases.html	0.86	0.68	1.00	0.80
/support/support.html	0.91	0.66	1.00	0.90
/training/training.html	0.85	0.69	0.96	0.80

[github-actions]

PR Summary

This PR introduces functional enhancements in the model development notebook and updates documentation for a cumulative prediction probabilities metric. In the model development notebook, the changes include:

• Importing GridSearchCV from scikit-learn to enable hyperparameter tuning.
• Adjusting the logistic regression parameters by replacing the previous penalty parameters ("l1" and "l2") with a more flexible approach using the 'l1_ratio' parameter, thereby enabling an elastic net penalty via the 'saga' solver.
• Refactoring the grid search setup to explicitly instantiate and configure LogisticRegression with increased maximum iterations and the appropriate penalty for using l1_ratio.

Additionally, a new test notebook file (test.ipynb) has been added to serve as a lightweight demonstration/example. The documentation file for cumulative prediction probabilities has also been updated to remove references to evaluation on separate training and test data and instead focus on a single provided dataset, with clarifications in the list of risk signs and strengths of the approach.

Test Suggestions

• Run the updated model development notebook to ensure GridSearchCV executes correctly with the new parameters.
• Verify that the LogisticRegression model converges successfully and returns expected best parameters.
• Execute the new test notebook to confirm it renders and functions as expected.
• Review the cumulative prediction probabilities visualization to ensure it appropriately reflects the changes in data plotting and documentation adjustments.

[github-actions]

Validate docs site

✓ INFO: A live preview of the docs site is available — Open the preview

[github-actions]

Execute training notebooks for PRs

✓ INFO: Live previews are available —

• Open model development series
• Open model validation series