correlation_view

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Axis correlation_view on sub-layer L1_5_E_correlation_pre_cleaning (layer l1_5).

Sub-layer

L1_5_E_correlation_pre_cleaning

Axis metadata

  • Default: 'none'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 4 option(s)

  • Future: 0 option(s)

Options

clustered_heatmap – operational

Clustered heatmap with hierarchical reorder of rows and columns.

L1.5.E correlation visualisation clustered_heatmap.

This option configures the correlation_view axis on the L1_5_E_correlation_pre_cleaning sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_E_correlation_pre_cleaning/ alongside the other selected views.

When to use

Large panels where cluster structure aids reading; reveals correlated variable blocks.

References

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

Related options: full_matrix, top_k_per_target, none

Last reviewed 2026-05-05 by macroforecast author.

full_matrix – operational

Full N×N correlation matrix as a heatmap.

L1.5.E correlation visualisation full_matrix.

This option configures the correlation_view axis on the L1_5_E_correlation_pre_cleaning sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_E_correlation_pre_cleaning/ alongside the other selected views.

When to use

Small panels (N < 50) where every pairwise correlation fits on one figure.

References

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

Related options: clustered_heatmap, top_k_per_target, none

Last reviewed 2026-05-05 by macroforecast author.

none – operational

Skip correlation diagnostics entirely.

L1.5.E correlation visualisation none.

This option configures the correlation_view axis on the L1_5_E_correlation_pre_cleaning sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_E_correlation_pre_cleaning/ alongside the other selected views.

When to use

Already covered by upstream EDA; reducing diagnostic surface.

References

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

Related options: full_matrix, clustered_heatmap, top_k_per_target

Last reviewed 2026-05-05 by macroforecast author.

top_k_per_target – operational

Top-k highest-|ρ| predictors per target.

L1.5.E correlation visualisation top_k_per_target.

This option configures the correlation_view axis on the L1_5_E_correlation_pre_cleaning sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_E_correlation_pre_cleaning/ alongside the other selected views.

When to use

Quickly identifying the most-correlated predictors when N is too large for a full matrix.

References

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

Related options: full_matrix, clustered_heatmap, none

Last reviewed 2026-05-05 by macroforecast author.