ensemble_view

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Axis ensemble_view on sub-layer L4_5_E_ensemble_diagnostics (layer l4_5).

Sub-layer

L4_5_E_ensemble_diagnostics

Axis metadata

  • Default: 'multi'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 4 option(s)

  • Future: 0 option(s)

Options

member_contribution – operational

Per-member contribution to forecast variance.

L4.5.E ensemble view member_contribution.

This option configures the ensemble_view axis on the L4_5_E_ensemble_diagnostics sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_E_ensemble_diagnostics/ alongside the other selected views.

When to use

Identifying free-rider members that contribute little to the ensemble’s predictive variance.

References

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

Related options: weights_over_time, weight_concentration, multi

Last reviewed 2026-05-05 by macroforecast author.

multi – operational

Render every ensemble diagnostic together.

L4.5.E ensemble view multi.

This option configures the ensemble_view axis on the L4_5_E_ensemble_diagnostics sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_E_ensemble_diagnostics/ alongside the other selected views.

When to use

Default rich ensemble audit. Activates the multi branch on L4.5.ensemble_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: weights_over_time, weight_concentration, member_contribution

Last reviewed 2026-05-05 by macroforecast author.

weight_concentration – operational

Herfindahl / entropy of ensemble weights.

L4.5.E ensemble view weight_concentration.

This option configures the ensemble_view axis on the L4_5_E_ensemble_diagnostics sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_E_ensemble_diagnostics/ alongside the other selected views.

When to use

Quantifying ensemble diversity; concentrated weights = under-diversified ensemble.

References

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

Related options: weights_over_time, member_contribution, multi

Last reviewed 2026-05-05 by macroforecast author.

weights_over_time – operational

Time-series of ensemble weights.

L4.5.E ensemble view weights_over_time.

This option configures the ensemble_view axis on the L4_5_E_ensemble_diagnostics sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_E_ensemble_diagnostics/ alongside the other selected views.

When to use

Tracking which member dominates over time; pairs with the L7 rolling_recompute for stability analysis.

References

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

Related options: weight_concentration, member_contribution, multi

Last reviewed 2026-05-05 by macroforecast author.