selection_view#

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Axis selection_view on sub-layer L3_5_E_selected_features_post_selection (layer l3_5).

Sub-layer#

L3_5_E_selected_features_post_selection

Axis metadata#

  • Default: 'multi'

  • Sweepable: False

  • Status: operational

Operational status summary#

  • Operational: 5 option(s)

  • Future: 0 option(s)

Options#

multi – operational#

Render every selection view.

L3.5.E selection view multi.

This option configures the selection_view axis on the L3_5_E_selected_features_post_selection sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_E_selected_features_post_selection/ alongside the other selected views.

When to use

Default rich audit. Activates the multi branch on L3.5.selection_view; combine with related options on the same sub-layer for a comprehensive diagnostic.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: selected_list, selection_count_per_origin, selection_stability

Last reviewed 2026-05-05 by macroforecast author.

none – operational#

(no schema description for none)

TBD: option doc not yet authored for this value. The encyclopedia falls back to the bare schema description above. PRs adding a full OptionDoc entry under macroforecast/scaffold/option_docs/l3_5.py are welcome.

selected_list – operational#

List of selected features per OOS origin.

L3.5.E selection view selected_list.

This option configures the selection_view axis on the L3_5_E_selected_features_post_selection sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_E_selected_features_post_selection/ alongside the other selected views.

When to use

Cheapest readout; the raw record of feature-selection decisions.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: selection_count_per_origin, selection_stability, multi

Last reviewed 2026-05-05 by macroforecast author.

selection_count_per_origin – operational#

Count of selected features per OOS origin.

L3.5.E selection view selection_count_per_origin.

This option configures the selection_view axis on the L3_5_E_selected_features_post_selection sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_E_selected_features_post_selection/ alongside the other selected views.

When to use

Detecting selection volatility; large variation across origins flags an unstable selection process.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: selected_list, selection_stability, multi

Last reviewed 2026-05-05 by macroforecast author.

selection_stability – operational#

Jaccard / Kuncheva-style stability across origins.

L3.5.E selection view selection_stability.

This option configures the selection_view axis on the L3_5_E_selected_features_post_selection sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_E_selected_features_post_selection/ alongside the other selected views.

When to use

Quantifying selection robustness; high stability is a positive indicator for the feature-selection method.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: selected_list, selection_count_per_origin, multi

Last reviewed 2026-05-05 by macroforecast author.